From 3e6537d04a0e3acc2aed9e37e646c14ec28df91d Mon Sep 17 00:00:00 2001 From: Michael Noseworthy Date: Thu, 5 Dec 2024 11:07:22 -0500 Subject: [PATCH 01/15] Initial factory commit --- .../lab_tasks/direct/factory/__init__.py | 18 + .../direct/factory/agents/__init__.py | 0 .../factory/agents/rl_games_ppo_cfg.yaml | 118 +++ .../direct/factory/factory_control.py | 406 ++++++++ .../lab_tasks/direct/factory/factory_env.py | 918 ++++++++++++++++++ .../direct/factory/factory_env_cfg.py | 215 ++++ .../direct/factory/factory_tasks_cfg.py | 457 +++++++++ .../direct/factory/torch_jit_utils.py | 61 ++ source/standalone/workflows/rl_games/play.py | 2 +- 9 files changed, 2194 insertions(+), 1 deletion(-) create mode 100644 source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/__init__.py create mode 100644 source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/agents/__init__.py create mode 100644 source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/agents/rl_games_ppo_cfg.yaml create mode 100644 source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_control.py create mode 100644 source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py create mode 100644 source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py create mode 100644 source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py create mode 100644 source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/torch_jit_utils.py diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/__init__.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/__init__.py new file mode 100644 index 0000000000..fe255eab5f --- /dev/null +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/__init__.py @@ -0,0 +1,18 @@ +import gymnasium as gym + +from . import agents +from .factory_env import FactoryEnv, FactoryEnvCfg + +## +# Register Gym environments. +## + +gym.register( + id="Factory-Direct-v0", + entry_point="omni.isaac.lab_tasks.direct.factory:FactoryEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": FactoryEnvCfg, + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml" + }, +) diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/agents/__init__.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/agents/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/agents/rl_games_ppo_cfg.yaml b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/agents/rl_games_ppo_cfg.yaml new file mode 100644 index 0000000000..4f580cc2e7 --- /dev/null +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,118 @@ +params: + seed: 0 + algo: + name: a2c_continuous + + env: + clip_actions: 1.0 + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + + space: + continuous: + mu_activation: None + sigma_activation: None + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: False + mlp: + units: [512, 128, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + rnn: + name: lstm + units: 1024 + layers: 2 + before_mlp: True + concat_input: True + layer_norm: True + + load_checkpoint: False + load_path: "" + + config: + name: Factory + device: cuda:0 + full_experiment_name: test + env_name: rlgpu + multi_gpu: False + ppo: True + mixed_precision: True + normalize_input: True + normalize_value: True + value_bootstrap: True + num_actors: 128 + reward_shaper: + scale_value: 1.0 + normalize_advantage: True + gamma: 0.995 + tau: 0.95 + learning_rate: 1.0e-4 + lr_schedule: adaptive + schedule_type: standard + kl_threshold: 0.008 + score_to_win: 20000 + max_epochs: 200 + save_best_after: 10 + save_frequency: 100 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.0 # 0.0001 # 0.0 + truncate_grads: True + e_clip: 0.2 + horizon_length: 128 + minibatch_size: 512 # batch size = num_envs * horizon_length; minibatch_size = batch_size / num_minibatches + mini_epochs: 4 + critic_coef: 2 + clip_value: True + seq_length: 128 + bounds_loss_coef: 0.0001 + + central_value_config: + minibatch_size: 512 + mini_epochs: 4 + learning_rate: 1e-4 + lr_schedule: adaptive + kl_threshold: 0.008 + clip_value: True + normalize_input: True + truncate_grads: True + + network: + name: actor_critic + central_value: True + + mlp: + units: [512, 128, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + rnn: + name: lstm + units: 1024 + layers: 2 + before_mlp: True + concat_input: True + layer_norm: True + player: + deterministic: False + diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_control.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_control.py new file mode 100644 index 0000000000..cf3e331c9a --- /dev/null +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_control.py @@ -0,0 +1,406 @@ +# Copyright (c) 2021-2022, NVIDIA Corporation +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# 3. Neither the name of the copyright holder nor the names of its +# contributors may be used to endorse or promote products derived from +# this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +"""Factory: control module. + +Imported by base, environment, and task classes. Not directly executed. +""" + +import math +import torch + +import omni.isaac.core.utils.torch as torch_utils + + +def compute_dof_pos_target(cfg_ctrl, + arm_dof_pos, + fingertip_midpoint_pos, + fingertip_midpoint_quat, + jacobian, + ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat, + ctrl_target_gripper_dof_pos, + device): + """Compute Franka DOF position target to move fingertips towards target pose.""" + + ctrl_target_dof_pos = torch.zeros((cfg_ctrl['num_envs'], 9), device=device) + + pos_error, axis_angle_error = get_pose_error( + fingertip_midpoint_pos=fingertip_midpoint_pos, + fingertip_midpoint_quat=fingertip_midpoint_quat, + ctrl_target_fingertip_midpoint_pos=ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat=ctrl_target_fingertip_midpoint_quat, + jacobian_type=cfg_ctrl['jacobian_type'], + rot_error_type='axis_angle') + + delta_fingertip_pose = torch.cat((pos_error, axis_angle_error), dim=1) + delta_arm_dof_pos = _get_delta_dof_pos(delta_pose=delta_fingertip_pose, + ik_method=cfg_ctrl['ik_method'], + jacobian=jacobian, + device=device) + + ctrl_target_dof_pos[:, 0:7] = arm_dof_pos + delta_arm_dof_pos + ctrl_target_dof_pos[:, 7:9] = ctrl_target_gripper_dof_pos # gripper finger joints + + return ctrl_target_dof_pos + + +def compute_dof_torque(cfg, + dof_pos, + dof_vel, + fingertip_midpoint_pos, + fingertip_midpoint_quat, + fingertip_midpoint_linvel, + fingertip_midpoint_angvel, + jacobian, + arm_mass_matrix, + ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat, + task_prop_gains, + task_deriv_gains, + device): + """Compute Franka DOF torque to move fingertips towards target pose.""" + # References: + # 1) https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf + # 2) Modern Robotics + + num_envs = cfg.scene.num_envs + dof_torque = torch.zeros((num_envs, dof_pos.shape[1]), device=device) + task_wrench = torch.zeros((num_envs, 6), device=device) + + pos_error, axis_angle_error = get_pose_error( + fingertip_midpoint_pos=fingertip_midpoint_pos, + fingertip_midpoint_quat=fingertip_midpoint_quat, + ctrl_target_fingertip_midpoint_pos=ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat=ctrl_target_fingertip_midpoint_quat, + jacobian_type='geometric', + rot_error_type='axis_angle') + delta_fingertip_pose = torch.cat((pos_error, axis_angle_error), dim=1) + + # Set tau = k_p * task_pos_error - k_d * task_vel_error (building towards eq. 3.96-3.98) + task_wrench_motion = _apply_task_space_gains( + delta_fingertip_pose=delta_fingertip_pose, + fingertip_midpoint_linvel=fingertip_midpoint_linvel, + fingertip_midpoint_angvel=fingertip_midpoint_angvel, + task_prop_gains=task_prop_gains, + task_deriv_gains=task_deriv_gains + ) + task_wrench += task_wrench_motion + + # Set tau = J^T * tau, i.e., map tau into joint space as desired + jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2) + dof_torque[:, 0:7] = (jacobian_T @ task_wrench.unsqueeze(-1)).squeeze(-1) + + # adapted from https://gitlab-master.nvidia.com/carbon-gym/carbgym/-/blob/b4bbc66f4e31b1a1bee61dbaafc0766bbfbf0f58/python/examples/franka_cube_ik_osc.py#L70-78 + # roboticsproceedings.org/rss07/p31.pdf + + # useful tensors + arm_mass_matrix_inv = torch.inverse(arm_mass_matrix) + jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2) + arm_mass_matrix_task = torch.inverse( + jacobian @ torch.inverse(arm_mass_matrix) @ jacobian_T) # ETH eq. 3.86; geometric Jacobian is assumed + j_eef_inv = arm_mass_matrix_task @ jacobian @ arm_mass_matrix_inv + default_dof_pos_tensor = torch.tensor( + cfg.ctrl.default_dof_pos_tensor, + device=device + ).repeat((num_envs, 1)) + # nullspace computation + distance_to_default_dof_pos = default_dof_pos_tensor - dof_pos[:, :7] + distance_to_default_dof_pos = ((distance_to_default_dof_pos + math.pi) % (2 * math.pi) - math.pi) # normalize to [-pi, pi] + u_null = cfg.ctrl.kd_null * -dof_vel[:, :7] + cfg.ctrl.kp_null * distance_to_default_dof_pos + u_null = arm_mass_matrix @ u_null.unsqueeze(-1) + torque_null = (torch.eye(7, device=device).unsqueeze(0) - torch.transpose(jacobian, 1, 2) @ j_eef_inv) @ u_null + dof_torque[:, 0:7] += torque_null.squeeze(-1) + + # TODO: Verify it's okay to no longer do gripper control here. + dof_torque = torch.clamp(dof_torque, min=-100.0, max=100.0) + return dof_torque, task_wrench + + +def get_pose_error(fingertip_midpoint_pos, + fingertip_midpoint_quat, + ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat, + jacobian_type, + rot_error_type): + """Compute task-space error between target Franka fingertip pose and current pose.""" + # Reference: https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf + + # Compute pos error + pos_error = ctrl_target_fingertip_midpoint_pos - fingertip_midpoint_pos + + # Compute rot error + if jacobian_type == 'geometric': # See example 2.9.8; note use of J_g and transformation between rotation vectors + # Compute quat error (i.e., difference quat) + # Reference: https://personal.utdallas.edu/~sxb027100/dock/quat.html + + # Check for shortest path using quaternion dot product. + quat_dot = (ctrl_target_fingertip_midpoint_quat*fingertip_midpoint_quat).sum(dim=1, keepdim=True) + ctrl_target_fingertip_midpoint_quat = torch.where( + quat_dot.expand(-1, 4) >= 0, + ctrl_target_fingertip_midpoint_quat, + -ctrl_target_fingertip_midpoint_quat + ) + + fingertip_midpoint_quat_norm = torch_utils.quat_mul(fingertip_midpoint_quat, + torch_utils.quat_conjugate(fingertip_midpoint_quat))[:, 0] # scalar component + fingertip_midpoint_quat_inv = torch_utils.quat_conjugate( + fingertip_midpoint_quat) / fingertip_midpoint_quat_norm.unsqueeze(-1) + quat_error = torch_utils.quat_mul(ctrl_target_fingertip_midpoint_quat, fingertip_midpoint_quat_inv) + + # Convert to axis-angle error + axis_angle_error = axis_angle_from_quat(quat_error) + + elif jacobian_type == 'analytic': # See example 2.9.7; note use of J_a and difference of rotation vectors + # Compute axis-angle error + axis_angle_error = axis_angle_from_quat(ctrl_target_fingertip_midpoint_quat)\ + - axis_angle_from_quat(fingertip_midpoint_quat) + + if rot_error_type == 'quat': + return pos_error, quat_error + elif rot_error_type == 'axis_angle': + return pos_error, axis_angle_error + + +def _get_wrench_error(left_finger_force, + right_finger_force, + ctrl_target_fingertip_contact_wrench, + num_envs, + device): + """Compute task-space error between target Franka fingertip contact wrench and current wrench.""" + + fingertip_contact_wrench = torch.zeros((num_envs, 6), device=device) + + fingertip_contact_wrench[:, 0:3] = left_finger_force + right_finger_force # net contact force on fingers + # Cols 3 to 6 are all zeros, as we do not have enough information + + force_error = ctrl_target_fingertip_contact_wrench[:, 0:3] - (-fingertip_contact_wrench[:, 0:3]) + torque_error = ctrl_target_fingertip_contact_wrench[:, 3:6] - (-fingertip_contact_wrench[:, 3:6]) + + return force_error, torque_error + + +def _get_delta_dof_pos(delta_pose, ik_method, jacobian, device): + """Get delta Franka DOF position from delta pose using specified IK method.""" + # References: + # 1) https://www.cs.cmu.edu/~15464-s13/lectures/lecture6/iksurvey.pdf + # 2) https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf (p. 47) + + if ik_method == 'pinv': # Jacobian pseudoinverse + k_val = 1.0 + jacobian_pinv = torch.linalg.pinv(jacobian) + delta_dof_pos = k_val * jacobian_pinv @ delta_pose.unsqueeze(-1) + delta_dof_pos = delta_dof_pos.squeeze(-1) + + elif ik_method == 'trans': # Jacobian transpose + k_val = 1.0 + jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2) + delta_dof_pos = k_val * jacobian_T @ delta_pose.unsqueeze(-1) + delta_dof_pos = delta_dof_pos.squeeze(-1) + + elif ik_method == 'dls': # damped least squares (Levenberg-Marquardt) + lambda_val = 0.1 # 0.1 + jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2) + lambda_matrix = (lambda_val ** 2) * torch.eye(n=jacobian.shape[1], device=device) + delta_dof_pos = jacobian_T @ torch.inverse(jacobian @ jacobian_T + lambda_matrix) @ delta_pose.unsqueeze(-1) + delta_dof_pos = delta_dof_pos.squeeze(-1) + + elif ik_method == 'svd': # adaptive SVD + k_val = 1.0 + U, S, Vh = torch.linalg.svd(jacobian) + S_inv = 1. / S + min_singular_value = 1.0e-5 + S_inv = torch.where(S > min_singular_value, S_inv, torch.zeros_like(S_inv)) + jacobian_pinv = torch.transpose(Vh, dim0=1, dim1=2)[:, :, :6] @ torch.diag_embed(S_inv) @ torch.transpose(U, dim0=1, dim1=2) + delta_dof_pos = k_val * jacobian_pinv @ delta_pose.unsqueeze(-1) + delta_dof_pos = delta_dof_pos.squeeze(-1) + + return delta_dof_pos + + +def _apply_task_space_gains(delta_fingertip_pose, + fingertip_midpoint_linvel, + fingertip_midpoint_angvel, + task_prop_gains, + task_deriv_gains): + """Interpret PD gains as task-space gains. Apply to task-space error.""" + + task_wrench = torch.zeros_like(delta_fingertip_pose) + + # Apply gains to lin error components + lin_error = delta_fingertip_pose[:, 0:3] + task_wrench[:, 0:3] = task_prop_gains[:, 0:3] * lin_error + \ + task_deriv_gains[:, 0:3] * (0.0 - fingertip_midpoint_linvel) + + # Apply gains to rot error components + rot_error = delta_fingertip_pose[:, 3:6] + task_wrench[:, 3:6] = task_prop_gains[:, 3:6] * rot_error + \ + task_deriv_gains[:, 3:6] * (0.0 - fingertip_midpoint_angvel) + return task_wrench + + +def get_analytic_jacobian(fingertip_quat, fingertip_jacobian, num_envs, device): + """Convert geometric Jacobian to analytic Jacobian.""" + # Reference: https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf + # NOTE: Gym returns world-space geometric Jacobians by default + + batch = num_envs + + # Overview: + # x = [x_p; x_r] + # From eq. 2.189 and 2.192, x_dot = J_a @ q_dot = (E_inv @ J_g) @ q_dot + # From eq. 2.191, E = block(E_p, E_r); thus, E_inv = block(E_p_inv, E_r_inv) + # Eq. 2.12 gives an expression for E_p_inv + # Eq. 2.107 gives an expression for E_r_inv + + # Compute E_inv_top (i.e., [E_p_inv, 0]) + I = torch.eye(3, device=device) + E_p_inv = I.repeat((batch, 1)).reshape(batch, 3, 3) + E_inv_top = torch.cat((E_p_inv, torch.zeros((batch, 3, 3), device=device)), dim=2) + + # Compute E_inv_bottom (i.e., [0, E_r_inv]) + fingertip_axis_angle = axis_angle_from_quat(fingertip_quat) + fingertip_axis_angle_cross = get_skew_symm_matrix(fingertip_axis_angle, device=device) + fingertip_angle = torch.linalg.vector_norm(fingertip_axis_angle, dim=1) + factor_1 = 1 / (fingertip_angle ** 2) + factor_2 = 1 - fingertip_angle * 0.5 * torch.sin(fingertip_angle) / (1 - torch.cos(fingertip_angle)) + factor_3 = factor_1 * factor_2 + E_r_inv = I \ + - 1 * 0.5 * fingertip_axis_angle_cross \ + + (fingertip_axis_angle_cross @ fingertip_axis_angle_cross) * factor_3.unsqueeze(-1).repeat((1, 3 * 3)).reshape((batch, 3, 3)) + E_inv_bottom = torch.cat((torch.zeros((batch, 3, 3), device=device), E_r_inv), dim=2) + + E_inv = torch.cat((E_inv_top.reshape((batch, 3 * 6)), E_inv_bottom.reshape((batch, 3 * 6))), dim=1).reshape((batch, 6, 6)) + + J_a = E_inv @ fingertip_jacobian + + return J_a + + +def get_skew_symm_matrix(vec, device): + """Convert vector to skew-symmetric matrix.""" + # Reference: https://en.wikipedia.org/wiki/Cross_product#Conversion_to_matrix_multiplication + + batch = vec.shape[0] + I = torch.eye(3, device=device) + skew_symm = torch.transpose(torch.cross(vec.repeat((1, 3)).reshape((batch * 3, 3)), + I.repeat((batch, 1))) + .reshape(batch, 3, 3), + dim0=1, + dim1=2) + + return skew_symm + + +def translate_along_local_z(pos, quat, offset, device): + """Translate global body position along local Z-axis and express in global coordinates.""" + + num_vecs = pos.shape[0] + offset_vec = offset * torch.tensor([0.0, 0.0, 1.0], device=device).repeat((num_vecs, 1)) + _, translated_pos = torch_utils.tf_combine(q1=quat, + t1=pos, + q2=torch.tensor([1.0, 0.0, 0.0, 0.0], device=device).repeat((num_vecs, 1)), + t2=offset_vec) + + return translated_pos + + +def translate_along_local_xyz(pos, quat, offset_vec, device): + """Translate global body position along local frame and express in global coordinates.""" + + num_vecs = pos.shape[0] + _, translated_pos = torch_utils.tf_combine(q1=quat, + t1=pos, + q2=torch.tensor([1.0, 0.0, 0.0, 0.0], device=device).repeat((num_vecs, 1)), + t2=offset_vec) + + return translated_pos + + +def axis_angle_from_euler(euler): + """Convert tensor of Euler angles to tensor of axis-angles.""" + + quat = torch_utils.quat_from_euler_xyz(roll=euler[:, 0], pitch=euler[:, 1], yaw=euler[:, 2]) + quat = quat * torch.sign(quat[:, 0]).unsqueeze(-1) # smaller rotation + axis_angle = axis_angle_from_quat(quat) + + return axis_angle + + +def axis_angle_from_quat(quat, eps=1.0e-6): + """Convert tensor of quaternions to tensor of axis-angles.""" + # Reference: https://github.com/facebookresearch/pytorch3d/blob/bee31c48d3d36a8ea268f9835663c52ff4a476ec/pytorch3d/transforms/rotation_conversions.py#L516-L544 + + mag = torch.linalg.norm(quat[:, 1:], dim=1) + half_angle = torch.atan2(mag, quat[:, 0]) + angle = 2.0 * half_angle + sin_half_angle_over_angle = torch.where(torch.abs(angle) > eps, + torch.sin(half_angle) / angle, + 1. / 2. - angle ** 2.0 / 48) + + axis_angle = quat[:, 1:] / sin_half_angle_over_angle.unsqueeze(-1) + + return axis_angle + + +def axis_angle_from_quat_naive(quat): + """Convert tensor of quaternions to tensor of axis-angles.""" + # Reference: https://en.wikipedia.org/wiki/quats_and_spatial_rotation#Recovering_the_axis-angle_representation + # NOTE: Susceptible to undesirable behavior due to divide-by-zero + + mag = torch.linalg.vector_norm(quat[:, 1:], dim=1) # zero when quat = [0, 0, 0, 1] + axis = quat[:, 1:] / mag.unsqueeze(-1) + angle = 2.0 * torch.atan2(mag, quat[:, 0]) + axis_angle = axis * angle.unsqueeze(-1) + + return axis_angle + + +def get_rand_quat(num_quats, device): + """Generate tensor of random quaternions.""" + # Reference: http://planning.cs.uiuc.edu/node198.html + + u = torch.rand((num_quats, 3), device=device) + quat = torch.zeros((num_quats, 4), device=device) + quat[:, 1] = torch.sqrt(1 - u[:, 0]) * torch.sin(2 * math.pi * u[:, 1]) + quat[:, 2] = torch.sqrt(1 - u[:, 0]) * torch.cos(2 * math.pi * u[:, 1]) + quat[:, 3] = torch.sqrt(u[:, 0]) * torch.sin(2 * math.pi * u[:, 2]) + quat[:, 0] = torch.sqrt(u[:, 0]) * torch.cos(2 * math.pi * u[:, 2]) + + return quat + + +def get_nonrand_quat(num_quats, rot_perturbation, device): + """Generate tensor of non-random quaternions by composing random Euler rotations.""" + + quat = torch_utils.quat_from_euler_xyz( + torch.rand((num_quats, 1), device=device).squeeze() * rot_perturbation * 2.0 - rot_perturbation, + torch.rand((num_quats, 1), device=device).squeeze() * rot_perturbation * 2.0 - rot_perturbation, + torch.rand((num_quats, 1), device=device).squeeze() * rot_perturbation * 2.0 - rot_perturbation) + + return quat diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py new file mode 100644 index 0000000000..78a86110ed --- /dev/null +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py @@ -0,0 +1,918 @@ +import numpy as np +import torch + +import carb +import omni.isaac.core.utils.torch as torch_utils +import omni.isaac.lab.sim as sim_utils +from omni.isaac.lab.assets import Articulation, RigidObject +from omni.isaac.lab.envs import DirectRLEnv +from omni.isaac.lab.sim.spawners.from_files import GroundPlaneCfg, spawn_ground_plane + + +from . import factory_control as fc +from .factory_env_cfg import FactoryEnvCfg, OBS_DIM_CFG, STATE_DIM_CFG +from .torch_jit_utils import quat_to_angle_axis + + +class FactoryEnv(DirectRLEnv): + cfg: FactoryEnvCfg + + def __init__(self, cfg: FactoryEnvCfg, render_mode: str | None = None, **kwargs): + # Update number of obs/states + cfg.observation_space = sum([OBS_DIM_CFG[obs] for obs in cfg.obs_order]) + cfg.state_space = sum([STATE_DIM_CFG[state] for state in cfg.state_order]) + cfg.observation_space += cfg.action_space + cfg.state_space += cfg.action_space + self.cfg_task = cfg.tasks[cfg.task_name] + + super().__init__(cfg, render_mode, **kwargs) + + self._set_body_inertias() + self._init_tensors() + self._set_default_dynamics_parameters() + self._compute_intermediate_values(dt=self.physics_dt) + + def _set_body_inertias(self): + """ Note: this is to account for the asset_options.armature parameter in IGE. """ + inertias = self._robot.root_physx_view.get_inertias() + offset = torch.zeros_like(inertias) + offset[:, :, [0, 4, 8]] += 0.01 + new_inertias = inertias + offset + self._robot.root_physx_view.set_inertias(new_inertias, torch.arange(self.num_envs)) + + def _set_default_dynamics_parameters(self): + """ Set parameters defining dynamic interactions. """ + self.default_gains = torch.tensor( + self.cfg.ctrl.default_task_prop_gains, + device=self.device + ).repeat((self.num_envs, 1)) + + self.pos_threshold = torch.tensor( + self.cfg.ctrl.pos_action_threshold, + device=self.device + ).repeat((self.num_envs, 1)) + self.rot_threshold = torch.tensor( + self.cfg.ctrl.rot_action_threshold, + device=self.device + ).repeat((self.num_envs, 1)) + + # Set masses and frictions. + self._set_friction(self._held_asset, self.cfg_task.held_asset_cfg.friction) + self._set_friction(self._fixed_asset, self.cfg_task.fixed_asset_cfg.friction) + self._set_friction(self._robot, self.cfg_task.robot_cfg.friction) + + def _set_friction(self, asset, value): + """ Update material properties for a given asset. """ + materials = asset.root_physx_view.get_material_properties() + materials[..., 0] = value # Static friction. + materials[..., 1] = value # Dynamic friction. + env_ids = torch.arange(self.scene.num_envs, device="cpu") + asset.root_physx_view.set_material_properties(materials, env_ids) + + def _init_tensors(self): + """ Initialize tensors once. """ + self.identity_quat = torch.tensor( + [1.0, 0.0, 0.0, 0.0], device=self.device + ).unsqueeze(0).repeat(self.num_envs, 1) + + # Control targets. + self.ctrl_target_joint_pos = torch.zeros( + (self.num_envs, self._robot.num_joints), device=self.device) + self.ctrl_target_fingertip_midpoint_pos = torch.zeros( + (self.num_envs, 3), device=self.device) + self.ctrl_target_fingertip_midpoint_quat = torch.zeros( + (self.num_envs, 4), device=self.device) + + # Fixed asset. + self.fixed_pos_action_frame = torch.zeros( + (self.num_envs, 3), device=self.device) + self.fixed_pos_obs_frame = torch.zeros( + (self.num_envs, 3), device=self.device) + self.init_fixed_pos_obs_noise = torch.zeros( + (self.num_envs, 3), device=self.device) + + # Held asset + held_base_x_offset = 0.0 + if self.cfg_task.name == 'peg_insertion': + held_base_z_offset = 0.0 + elif self.cfg_task.name == 'gear_meshing': + gear_base_offset = self._get_target_gear_base_offset() + held_base_x_offset = gear_base_offset[0] + held_base_z_offset = gear_base_offset[2] + elif self.cfg_task.name == 'nut_threading': + held_base_z_offset = self.cfg_task.fixed_asset_cfg.base_height + else: + raise NotImplementedError('Task not implemented') + + self.held_base_pos_local = torch.tensor( + [0.0, 0.0, 0.0], device=self.device + ).repeat((self.num_envs, 1)) + self.held_base_pos_local[:, 0] = held_base_x_offset + self.held_base_pos_local[:, 2] = held_base_z_offset + self.held_base_quat_local = self.identity_quat.clone().detach() + + self.held_base_pos = torch.zeros_like(self.held_base_pos_local) + self.held_base_quat = self.identity_quat.clone().detach() + + # Computer body indices. + self.left_finger_body_idx = self._robot.body_names.index('panda_leftfinger') + self.right_finger_body_idx = self._robot.body_names.index('panda_rightfinger') + self.fingertip_body_idx = self._robot.body_names.index('panda_fingertip_centered') + + # Tensors for finite-differencing. + self.last_update_timestamp = 0.0 # Note: This is for finite differencing body velocities. + self.prev_fingertip_pos = torch.zeros( + (self.num_envs, 3), device=self.device) + self.prev_fingertip_quat = self.identity_quat.clone() + self.prev_joint_pos = torch.zeros( + (self.num_envs, 7), device=self.device) + + # Keypoint tensors. + self.target_held_base_pos = torch.zeros( + (self.num_envs, 3), device=self.device) + self.target_held_base_quat = self.identity_quat.clone().detach() + + offsets = self._get_keypoint_offsets(self.cfg_task.num_keypoints) + self.keypoint_offsets = offsets * self.cfg_task.keypoint_scale + self.keypoints_held = torch.zeros( + (self.num_envs, self.cfg_task.num_keypoints, 3),device=self.device) + self.keypoints_fixed = torch.zeros_like(self.keypoints_held, device=self.device) + + # Used to compute target poses. + self.fixed_success_pos_local = torch.zeros( + (self.num_envs, 3), device=self.device) + if self.cfg_task.name == 'peg_insertion': + self.fixed_success_pos_local[:, 2] = 0.0 + elif self.cfg_task.name == 'gear_meshing': + gear_base_offset = self._get_target_gear_base_offset() + self.fixed_success_pos_local[:, 0] = gear_base_offset[0] + self.fixed_success_pos_local[:, 2] = gear_base_offset[2] + elif self.cfg_task.name == 'nut_threading': + head_height = self.cfg_task.fixed_asset_cfg.base_height + shank_length = self.cfg_task.fixed_asset_cfg.height + thread_pitch = self.cfg_task.fixed_asset_cfg.thread_pitch + self.fixed_success_pos_local[:, 2] = head_height + shank_length - thread_pitch * 1.5 + else: + raise NotImplementedError('Task not implemented') + + self.ep_succeeded = torch.zeros((self.num_envs,), dtype=torch.long, device=self.device) + self.ep_success_times = torch.zeros((self.num_envs,), dtype=torch.long, device=self.device) + + def _get_keypoint_offsets(self, num_keypoints): + """ Get uniformly-spaced keypoints along a line of unit length, centered at 0. """ + keypoint_offsets = torch.zeros((num_keypoints, 3), device=self.device) + keypoint_offsets[:, -1] = torch.linspace(0.0, 1.0, num_keypoints, device=self.device) - 0.5 + + return keypoint_offsets + + def _setup_scene(self): + """ Initialize simulation scene. """ + spawn_ground_plane( + prim_path="/World/ground", + cfg=GroundPlaneCfg(), + translation=(0.0, 0.0, -0.4) + ) + self._robot = Articulation(self.cfg.robot) + self._table = RigidObject(self.cfg.table) + self._fixed_asset = RigidObject(self.cfg_task.fixed_asset) + self._held_asset = RigidObject(self.cfg_task.held_asset) + if self.cfg_task.name == 'gear_meshing': + self._small_gear_asset = RigidObject(self.cfg_task.small_gear_cfg) + self._large_gear_asset = RigidObject(self.cfg_task.large_gear_cfg) + + self.scene.clone_environments(copy_from_source=False) + self.scene.filter_collisions() + + self.scene.articulations["robot"] = self._robot + self.scene.rigid_objects["table"] = self._table + self.scene.rigid_objects["fixed_asset"] = self._fixed_asset + self.scene.rigid_objects["held_asset"] = self._held_asset + if self.cfg_task.name == 'gear_meshing': + self.scene.rigid_objects["small_gear"] = self._small_gear_asset + self.scene.rigid_objects["large_gear"] = self._large_gear_asset + + # add lights + light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) + light_cfg.func("/World/Light", light_cfg) + + def _compute_intermediate_values(self, dt): + """ Get values computed from raw tensors. This includes adding noise. """ + # TODO: A lot of these can probably only be set once? + self.fixed_pos = self._fixed_asset.data.root_pos_w - self.scene.env_origins + self.fixed_quat = self._fixed_asset.data.root_quat_w + + self.held_pos = self._held_asset.data.root_pos_w - self.scene.env_origins + self.held_quat = self._held_asset.data.root_quat_w + + self.fingertip_midpoint_pos = self._robot.data.body_pos_w[:, self.fingertip_body_idx] - self.scene.env_origins + self.fingertip_midpoint_quat = self._robot.data.body_quat_w[:, self.fingertip_body_idx] + self.fingertip_midpoint_linvel = self._robot.data.body_lin_vel_w[:, self.fingertip_body_idx] + self.fingertip_midpoint_angvel = self._robot.data.body_ang_vel_w[:, self.fingertip_body_idx] + + jacobians = self._robot.root_physx_view.get_jacobians() + + self.left_finger_jacobian = jacobians[:, self.left_finger_body_idx - 1, 0:6, 0:7] + self.right_finger_jacobian = jacobians[:, self.right_finger_body_idx - 1, 0:6, 0:7] + self.fingertip_midpoint_jacobian = (self.left_finger_jacobian + self.right_finger_jacobian) * 0.5 + self.arm_mass_matrix = self._robot.root_physx_view.get_mass_matrices()[:, 0:7, 0:7] + self.joint_pos = self._robot.data.joint_pos.clone() + self.joint_vel = self._robot.data.joint_vel.clone() + + # Finite-differencing results in more reliable velocity estimates. + self.ee_linvel_fd = (self.fingertip_midpoint_pos - self.prev_fingertip_pos) / dt + self.prev_fingertip_pos = self.fingertip_midpoint_pos.clone() + + # Add state differences if velocity isn't being added. + rot_diff_quat = torch_utils.quat_mul( + self.fingertip_midpoint_quat, + torch_utils.quat_conjugate(self.prev_fingertip_quat)) + rot_diff_quat *= torch.sign(rot_diff_quat[:, 0]).unsqueeze(-1) + rot_diff_aa = quat_to_angle_axis(rot_diff_quat) + + self.ee_angvel_fd = (rot_diff_aa[0].unsqueeze(-1) * rot_diff_aa[1]) / dt + self.prev_fingertip_quat = self.fingertip_midpoint_quat.clone() + + joint_diff = self.joint_pos[:, 0:7] - self.prev_joint_pos + self.joint_vel_fd = joint_diff / dt + self.prev_joint_pos = self.joint_pos[:, 0:7].clone() + + # Keypoint tensors. + self.held_base_quat[:], self.held_base_pos[:] = torch_utils.tf_combine( + self.held_quat, self.held_pos, + self.held_base_quat_local, self.held_base_pos_local) + self.target_held_base_quat[:], self.target_held_base_pos[:] = torch_utils.tf_combine( + self.fixed_quat, self.fixed_pos, + self.identity_quat, self.fixed_success_pos_local) + + # Compute pos of keypoints on held asset, and fixed asset in world frame + for idx, keypoint_offset in enumerate(self.keypoint_offsets): + self.keypoints_held[:, idx] = torch_utils.tf_combine( + self.held_base_quat, self.held_base_pos, + self.identity_quat, keypoint_offset.repeat(self.num_envs, 1))[1] + self.keypoints_fixed[:, idx] = torch_utils.tf_combine( + self.target_held_base_quat, self.target_held_base_pos, + self.identity_quat, keypoint_offset.repeat(self.num_envs, 1))[1] + + self.keypoint_dist = torch.norm( + self.keypoints_held - self.keypoints_fixed, + p=2, dim=-1).mean(-1) + self.last_update_timestamp = self._robot._data._sim_timestamp + + def _get_observations(self): + """ Get actor/critic inputs using assymetric critic. """ + noisy_fixed_pos = self.fixed_pos_obs_frame + self.init_fixed_pos_obs_noise + + prev_actions = self.actions.clone() + + obs_dict = { + 'fingertip_pos': self.fingertip_midpoint_pos, + 'fingertip_pos_rel_fixed': self.fingertip_midpoint_pos - noisy_fixed_pos, + 'fingertip_quat': self.fingertip_midpoint_quat, + 'ee_linvel': self.ee_linvel_fd, + 'ee_angvel': self.ee_angvel_fd, + 'prev_actions': prev_actions + } + + state_dict = { + 'fingertip_pos': self.fingertip_midpoint_pos, + 'fingertip_pos_rel_fixed': self.fingertip_midpoint_pos - self.fixed_pos_obs_frame, + 'fingertip_quat': self.fingertip_midpoint_quat, + 'ee_linvel': self.fingertip_midpoint_linvel, + 'ee_angvel': self.fingertip_midpoint_angvel, + 'joint_pos': self.joint_pos[:, 0:7], + 'held_pos': self.held_pos, + 'held_pos_rel_fixed': self.held_pos - self.fixed_pos_obs_frame, + 'held_quat': self.held_quat, + 'fixed_pos': self.fixed_pos, + 'fixed_quat': self.fixed_quat, + 'task_prop_gains': self.task_prop_gains, + 'pos_threshold': self.pos_threshold, + 'rot_threshold': self.rot_threshold, + 'prev_actions': prev_actions + } + obs_tensors = [obs_dict[obs_name] for obs_name in self.cfg.obs_order + ['prev_actions']] + obs_tensors = torch.cat(obs_tensors, dim=-1) + state_tensors = [state_dict[state_name] for state_name in self.cfg.state_order + ['prev_actions']] + state_tensors = torch.cat(state_tensors, dim=-1) + return {'policy': obs_tensors, 'critic': state_tensors} + + def _reset_buffers(self, env_ids): + """ Reset buffers. """ + self.ep_succeeded[env_ids] = 0 + + def _pre_physics_step(self, action): + """ Apply policy actions with smoothing. """ + env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1) + if len(env_ids) > 0: + self._reset_buffers(env_ids) + + self.actions = self.cfg.ctrl.ema_factor*action.clone().to(self.device) + (1-self.cfg.ctrl.ema_factor)*self.actions + + def close_gripper_in_place(self): + """ Keep gripper in current position as gripper closes. """ + actions = torch.zeros((self.num_envs, 6), device=self.device) + ctrl_target_gripper_dof_pos = 0.0 + + # Interpret actions as target pos displacements and set pos target + pos_actions = actions[:, 0:3] * self.pos_threshold + self.ctrl_target_fingertip_midpoint_pos = self.fingertip_midpoint_pos + pos_actions + + # Interpret actions as target rot (axis-angle) displacements + rot_actions = actions[:, 3:6] + + # Convert to quat and set rot target + angle = torch.norm(rot_actions, p=2, dim=-1) + axis = rot_actions / angle.unsqueeze(-1) + + rot_actions_quat = torch_utils.quat_from_angle_axis(angle, axis) + + rot_actions_quat = torch.where( + angle.unsqueeze(-1).repeat(1, 4) > 1.0e-6, + rot_actions_quat, + torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).repeat(self.num_envs, 1) + ) + self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_mul( + rot_actions_quat, + self.fingertip_midpoint_quat) + + target_euler_xyz = torch.stack( + torch_utils.get_euler_xyz(self.ctrl_target_fingertip_midpoint_quat), + dim=1) + target_euler_xyz[:, 0] = 3.14159 + target_euler_xyz[:, 1] = 0.0 + + self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz( + roll=target_euler_xyz[:, 0], + pitch=target_euler_xyz[:, 1], + yaw=target_euler_xyz[:, 2] + ) + + self.ctrl_target_gripper_dof_pos = ctrl_target_gripper_dof_pos + self.generate_ctrl_signals() + + def _apply_action(self): + """ Apply actions for policy as delta targets from current position. """ + # Get current yaw for success checking. + _, _, curr_yaw = torch_utils.get_euler_xyz(self.fingertip_midpoint_quat) + self.curr_yaw = torch.where(curr_yaw > np.deg2rad(235), curr_yaw - 2 * np.pi, curr_yaw) + + # Note: We use finite-differenced velocities for control and observations. + # Check if we need to re-compute velocities within the decimation loop. + if self.last_update_timestamp < self._robot._data._sim_timestamp: + self._compute_intermediate_values(dt=self.physics_dt) + + # Interpret actions as target pos displacements and set pos target + pos_actions = self.actions[:, 0:3] * self.pos_threshold + + # Interpret actions as target rot (axis-angle) displacements + rot_actions = self.actions[:, 3:6] + if self.cfg_task.unidirectional_rot: + rot_actions[:, 2] = -(rot_actions[:, 2] + 1.0) * 0.5 # [-1, 0] + rot_actions = rot_actions * self.rot_threshold + + self.ctrl_target_fingertip_midpoint_pos = self.fingertip_midpoint_pos + pos_actions + # To speed up learning, never allow the policy to move more than 5cm away from the base. + delta_pos = self.ctrl_target_fingertip_midpoint_pos - self.fixed_pos_action_frame + pos_error_clipped = torch.clip(delta_pos, -self.cfg.ctrl.pos_action_bounds[0], self.cfg.ctrl.pos_action_bounds[1]) + self.ctrl_target_fingertip_midpoint_pos = self.fixed_pos_action_frame + pos_error_clipped + + # Convert to quat and set rot target + angle = torch.norm(rot_actions, p=2, dim=-1) + axis = rot_actions / angle.unsqueeze(-1) + + rot_actions_quat = torch_utils.quat_from_angle_axis(angle, axis) + rot_actions_quat = torch.where( + angle.unsqueeze(-1).repeat(1, 4) > 1e-6, + rot_actions_quat, + torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).repeat( + self.num_envs, 1 + ) + ) + self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_mul(rot_actions_quat, self.fingertip_midpoint_quat) + + target_euler_xyz = torch.stack(torch_utils.get_euler_xyz(self.ctrl_target_fingertip_midpoint_quat), dim=1) + target_euler_xyz[:, 0] = 3.14159 # Restrict actions to be upright. + target_euler_xyz[:, 1] = 0.0 + + self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz( + roll=target_euler_xyz[:, 0], + pitch=target_euler_xyz[:, 1], + yaw=target_euler_xyz[:, 2] + ) + + self.ctrl_target_gripper_dof_pos = 0.0 + self.generate_ctrl_signals() + + def _set_gains(self, prop_gains, rot_deriv_scale=1.): + """ Set robot gains using critical damping. """ + self.task_prop_gains = prop_gains + self.task_deriv_gains = 2 * torch.sqrt(prop_gains) + self.task_deriv_gains[:, 3:6] /= rot_deriv_scale + + def generate_ctrl_signals(self): + """ Get Jacobian. Set Franka DOF position targets (fingers) or DOF torques (arm). """ + self.joint_torque, self.applied_wrench = fc.compute_dof_torque( + cfg=self.cfg, + dof_pos=self.joint_pos, + dof_vel=self.joint_vel,#_fd, + fingertip_midpoint_pos=self.fingertip_midpoint_pos, + fingertip_midpoint_quat=self.fingertip_midpoint_quat, + fingertip_midpoint_linvel=self.ee_linvel_fd, + fingertip_midpoint_angvel=self.ee_angvel_fd, + jacobian=self.fingertip_midpoint_jacobian, + arm_mass_matrix=self.arm_mass_matrix, + ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat, + task_prop_gains=self.task_prop_gains, + task_deriv_gains=self.task_deriv_gains, + device=self.device) + + # set target for gripper joints to use GYM's PD controller + self.ctrl_target_joint_pos[:, 7:9] = self.ctrl_target_gripper_dof_pos + self.joint_torque[:, 7:9] = 0.0 + + self._robot.set_joint_position_target(self.ctrl_target_joint_pos) + self._robot.set_joint_effort_target(self.joint_torque) + + def _get_dones(self): + """ Update intermediate values used for rewards and observations. """ + self._compute_intermediate_values(dt=self.physics_dt) + time_out = self.episode_length_buf >= self.max_episode_length - 1 + return time_out, time_out + + def _get_curr_successes(self, success_threshold, check_rot=False): + """ Get success mask at current timestep. """ + curr_successes = torch.zeros((self.num_envs,), dtype=torch.bool, device=self.device) + + xy_dist = torch.linalg.vector_norm( + self.target_held_base_pos[:, 0:2] - self.held_base_pos[:, 0:2], + dim=1) + z_disp = self.held_base_pos[:, 2] - self.target_held_base_pos[:, 2] + + is_centered = torch.where( + xy_dist < 0.0025, + torch.ones_like(curr_successes), + torch.zeros_like(curr_successes) + ) + # Height threshold to target + fixed_cfg = self.cfg_task.fixed_asset_cfg + if self.cfg_task.name == 'peg_insertion': + height_threshold = fixed_cfg.height * success_threshold + elif self.cfg_task.name == 'gear_meshing': + height_threshold = fixed_cfg.height * success_threshold + elif self.cfg_task.name == 'nut_threading': + height_threshold = fixed_cfg.thread_pitch * success_threshold + else: + raise NotImplementedError('Task not implemented') + is_close_or_below = torch.where(z_disp < height_threshold, + torch.ones_like(curr_successes), + torch.zeros_like(curr_successes)) + curr_successes = torch.logical_and(is_centered, is_close_or_below) + + if check_rot: + is_rotated = self.curr_yaw < self.cfg_task.ee_success_yaw + curr_successes = torch.logical_and(curr_successes, is_rotated) + + return curr_successes + + def _get_rewards(self): + """ Update rewards and compute success statistics. """ + # Get successful and failed envs at current timestep + check_rot = self.cfg_task.name == 'nut_threading' + curr_successes = self._get_curr_successes( + success_threshold=self.cfg_task.success_threshold, + check_rot=check_rot) + + rew_buf = self._update_rew_buf(curr_successes) + + # Only log episode success rates at the end of an episode. + if torch.any(self.reset_buf): + self.extras["successes"] = torch.count_nonzero(curr_successes) / self.num_envs + + # Get the time at which an episode first succeeds. + first_success = torch.logical_and(curr_successes, torch.logical_not(self.ep_succeeded)) + self.ep_succeeded[curr_successes] = 1 + + first_success_ids = first_success.nonzero(as_tuple=False).squeeze(-1) + self.ep_success_times[first_success_ids] = self.episode_length_buf[first_success_ids] + nonzero_success_ids = self.ep_success_times.nonzero(as_tuple=False).squeeze(-1) + + if len(nonzero_success_ids) > 0: # Only log for successful episodes. + success_times = self.ep_success_times[nonzero_success_ids].sum() / len(nonzero_success_ids) + self.extras["success_times"] = success_times + + self.prev_actions = self.actions.clone() + return rew_buf + + def _update_rew_buf(self, curr_successes): + """ Compute reward at current timestep. """ + rew_dict = {} + + # Keypoint rewards. + squashing_fn = lambda x, a, b: 1 / (torch.exp(a*x) + b + torch.exp(-a*x)) + a0, b0 = self.cfg_task.keypoint_coef_baseline + rew_dict['kp_baseline'] = squashing_fn(self.keypoint_dist, a0, b0) + # a1, b1 = 25, 2 + a1, b1 = self.cfg_task.keypoint_coef_coarse + rew_dict['kp_coarse'] = squashing_fn(self.keypoint_dist, a1, b1) + a2, b2 = self.cfg_task.keypoint_coef_fine + # a2, b2 = 300, 0 + rew_dict['kp_fine'] = squashing_fn(self.keypoint_dist, a2, b2) + + # Action penalties. + rew_dict['action_penalty'] = torch.norm(self.actions, p=2) + rew_dict['action_grad_penalty'] = torch.norm(self.actions - self.prev_actions, p=2, dim=-1) + rew_dict['curr_engaged'] = self._get_curr_successes( + success_threshold=self.cfg_task.engage_threshold, + check_rot=False).clone().float() + rew_dict['curr_successes'] = curr_successes.clone().float() + + rew_buf = rew_dict['kp_coarse'] + rew_dict['kp_baseline'] + rew_dict['kp_fine'] \ + - rew_dict['action_penalty'] * self.cfg_task.action_penalty_scale \ + - rew_dict['action_grad_penalty'] * self.cfg_task.action_grad_penalty_scale \ + + rew_dict['curr_engaged'] + rew_dict['curr_successes'] + + for rew_name, rew in rew_dict.items(): + self.extras[f'logs_rew_{rew_name}'] = rew.mean() + + return rew_buf + + def _reset_idx(self, env_ids): + """ + We assume all envs will always be reset at the same time. + """ + super()._reset_idx(env_ids) + + self._set_assets_to_default_pose(env_ids) + self._set_franka_to_default_pose( + joints=[1.5178e-03, -1.9651e-01, -1.4364e-03, -1.9761, -2.7717e-04, 1.7796, 7.8556e-01], + env_ids=env_ids) + self.step_sim_no_action() + + self.randomize_initial_state(env_ids) + + def _get_target_gear_base_offset(self): + """ Get offset of target gear from the gear base asset. """ + target_gear = self.cfg_task.target_gear + if target_gear == 'gear_large': + gear_base_offset = self.cfg_task.fixed_asset_cfg.large_gear_base_offset + elif target_gear == 'gear_medium': + gear_base_offset = self.cfg_task.fixed_asset_cfg.medium_gear_base_offset + elif target_gear == 'gear_small': + gear_base_offset = self.cfg_task.fixed_asset_cfg.small_gear_base_offset + else: + raise ValueError(f'{target_gear} not valid in this context!') + return gear_base_offset + + def _set_assets_to_default_pose(self, env_ids): + """ Move assets to default pose before randomization. """ + held_state = self._held_asset.data.default_root_state.clone()[env_ids] + held_state[:, 0:3] += self.scene.env_origins[env_ids] + held_state[:, 7:] = 0.0 + self._held_asset.write_root_state_to_sim(held_state, env_ids=env_ids) + self._held_asset.reset() + + fixed_state = self._fixed_asset.data.default_root_state.clone()[env_ids] + fixed_state[:, 0:3] += self.scene.env_origins[env_ids] + fixed_state[:, 7:] = 0.0 + self._fixed_asset.write_root_state_to_sim(fixed_state, env_ids=env_ids) + self._fixed_asset.reset() + + def set_pos_inverse_kinematics(self, env_ids): + """ Set robot joint position using DLS IK. """ + ik_time = 0.0 + while ik_time < 0.25: + # Compute error to target. + pos_error, axis_angle_error = fc.get_pose_error( + fingertip_midpoint_pos=self.fingertip_midpoint_pos[env_ids], + fingertip_midpoint_quat=self.fingertip_midpoint_quat[env_ids], + ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_midpoint_pos[env_ids], + ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat[env_ids], + jacobian_type='geometric', + rot_error_type='axis_angle') + + delta_hand_pose = torch.cat((pos_error, axis_angle_error), dim=-1) + + # Solve DLS problem. + delta_dof_pos = fc._get_delta_dof_pos( + delta_pose=delta_hand_pose, + ik_method='dls', + jacobian=self.fingertip_midpoint_jacobian[env_ids], + device=self.device) + self.joint_pos[env_ids, 0:7] += delta_dof_pos[:, 0:7] + self.joint_vel[env_ids, :] = torch.zeros_like(self.joint_pos[env_ids,]) + + self.ctrl_target_joint_pos[env_ids, 0:7] = self.joint_pos[env_ids, 0:7] + # Update dof state. + self._robot.write_joint_state_to_sim(self.joint_pos, self.joint_vel) + self._robot.reset() + self._robot.set_joint_position_target(self.ctrl_target_joint_pos) + + # Simulate and update tensors. + self.step_sim_no_action() + ik_time += self.physics_dt + + return pos_error, axis_angle_error + + def get_handheld_asset_relative_pose(self): + """ Get default relative pose between help asset and fingertip. """ + if self.cfg_task.name == 'peg_insertion': + held_asset_relative_pos = torch.zeros_like(self.held_base_pos_local) + held_asset_relative_pos[:, 2] = self.cfg_task.held_asset_cfg.height + held_asset_relative_pos[:, 2] -= self.cfg_task.robot_cfg.franka_fingerpad_length + elif self.cfg_task.name == 'gear_meshing': + held_asset_relative_pos = torch.zeros_like(self.held_base_pos_local) + gear_base_offset = self._get_target_gear_base_offset() + held_asset_relative_pos[:, 0] += gear_base_offset[0] + held_asset_relative_pos[:, 2] += gear_base_offset[2] + held_asset_relative_pos[:, 2] += self.cfg_task.held_asset_cfg.height / 2.0 * 1.1 + elif self.cfg_task.name == 'nut_threading': + held_asset_relative_pos = self.held_base_pos_local + else: + raise NotImplementedError('Task not implemented') + + held_asset_relative_quat = self.identity_quat + if self.cfg_task.name == 'nut_threading': + # Rotate along z-axis of frame for default position. + initial_rot_deg = self.cfg_task.held_asset_rot_init + rot_yaw_euler = torch.tensor( + [0., 0., initial_rot_deg * np.pi / 180.0], device=self.device + ).repeat(self.num_envs, 1) + held_asset_relative_quat = torch_utils.quat_from_euler_xyz( + roll=rot_yaw_euler[:, 0], + pitch=rot_yaw_euler[:, 1], + yaw=rot_yaw_euler[:, 2]) + + return held_asset_relative_pos, held_asset_relative_quat + + def _set_franka_to_default_pose(self, joints, env_ids): + """ Return Franka to its default joint position. """ + gripper_width = self.cfg_task.held_asset_cfg.diameter / 2 * 1.25 + joint_pos = self._robot.data.default_joint_pos[env_ids] + joint_pos[:, 7:] = gripper_width # MIMIC + joint_pos[:, :7] = torch.tensor(joints, device=self.device)[None, :] + joint_vel = torch.zeros_like(joint_pos) + joint_effort = torch.zeros_like(joint_pos) + self.ctrl_target_joint_pos[env_ids, :] = joint_pos + print(f'Resetting {len(env_ids)} envs...') + self._robot.set_joint_position_target(self.ctrl_target_joint_pos[env_ids], env_ids=env_ids) + self._robot.write_joint_state_to_sim(joint_pos, joint_vel, env_ids=env_ids) + self._robot.reset() + self._robot.set_joint_effort_target(joint_effort, env_ids=env_ids) + + self.step_sim_no_action() + + def step_sim_no_action(self): + """ Step the simulation without an action. Used for resets. """ + self.scene.write_data_to_sim() + self.sim.step(render=True) + self.scene.update(dt=self.physics_dt) + self._compute_intermediate_values(dt=self.physics_dt) + + def randomize_initial_state(self, env_ids): + """ Randomize initial state and perform any episode-level randomization. """ + # Disable gravity. + physics_sim_view = sim_utils.SimulationContext.instance().physics_sim_view + physics_sim_view.set_gravity(carb.Float3(0.0, 0.0, 0.0)) + + # (1.) Randomize fixed asset pose. + fixed_state = self._fixed_asset.data.default_root_state.clone()[env_ids] + # (1.a.) Position + rand_sample = torch.rand((len(env_ids), 3), dtype=torch.float32, device=self.device) + fixed_pos_init_rand = 2 * (rand_sample - 0.5) # [-1, 1] + fixed_asset_init_pos_rand = torch.tensor( + self.cfg_task.fixed_asset_init_pos_noise, + dtype=torch.float32, device=self.device) + fixed_pos_init_rand = fixed_pos_init_rand @ torch.diag(fixed_asset_init_pos_rand) + fixed_state[:, 0:3] += fixed_pos_init_rand + self.scene.env_origins[env_ids] + # (1.b.) Orientation + fixed_orn_init_yaw = np.deg2rad(self.cfg_task.fixed_asset_init_orn_deg) + fixed_orn_yaw_range = np.deg2rad(self.cfg_task.fixed_asset_init_orn_range_deg) + rand_sample = torch.rand((len(env_ids), 3), dtype=torch.float32, device=self.device) + fixed_orn_euler = fixed_orn_init_yaw + fixed_orn_yaw_range * rand_sample + fixed_orn_euler[:, 0:2] = 0. # Only change yaw. + fixed_orn_quat = torch_utils.quat_from_euler_xyz( + fixed_orn_euler[:, 0], + fixed_orn_euler[:, 1], + fixed_orn_euler[:, 2]) + fixed_state[:, 3:7] = fixed_orn_quat + # (1.c.) Velocity + fixed_state[:, 7:] = 0.0 # vel + # (1.d.) Update values. + self._fixed_asset.write_root_state_to_sim(fixed_state, env_ids=env_ids) + self._fixed_asset.reset() + + # (1.e.) Noisy position observation. + fixed_asset_pos_noise = torch.randn((len(env_ids), 3), dtype=torch.float32, device=self.device) + fixed_asset_pos_rand = torch.tensor( + self.cfg.obs_rand.fixed_asset_pos, + dtype=torch.float32, device=self.device) + fixed_asset_pos_noise = fixed_asset_pos_noise @ torch.diag(fixed_asset_pos_rand) + self.init_fixed_pos_obs_noise[:] = fixed_asset_pos_noise + + self.step_sim_no_action() + + # Compute the frame on the bolt that would be used as observation: fixed_pos_obs_frame + # For example, the tip of the bolt can be used as the observation frame + fixed_tip_pos_local = torch.zeros_like(self.fixed_pos) + fixed_tip_pos_local[:, 2] += self.cfg_task.fixed_asset_cfg.height + fixed_tip_pos_local[:, 2] += self.cfg_task.fixed_asset_cfg.base_height + if self.cfg_task.name == 'gear_meshing': + fixed_tip_pos_local[:, 0] = self._get_target_gear_base_offset()[0] + + _, fixed_tip_pos = torch_utils.tf_combine( + self.fixed_quat, self.fixed_pos, + self.identity_quat, fixed_tip_pos_local + ) + self.fixed_pos_obs_frame[:] = fixed_tip_pos + + # (2) Move gripper to randomizes location above fixed asset. Keep trying until IK succeeds. + # (a) get position vector to target + bad_envs = env_ids.clone() + ik_attempt = 0 + + hand_down_quat = torch.zeros((self.num_envs, 4), dtype=torch.float32, device=self.device) + self.hand_down_euler = torch.zeros((self.num_envs, 3), dtype=torch.float32, device=self.device) + while True: + n_bad = bad_envs.shape[0] + + above_fixed_pos = fixed_tip_pos.clone() + above_fixed_pos[:, 2] += self.cfg_task.hand_init_pos[2] + + rand_sample = torch.rand((n_bad, 3), dtype=torch.float32, device=self.device) + above_fixed_pos_rand = 2 * (rand_sample - 0.5) # [-1, 1] + hand_init_pos_rand = torch.tensor(self.cfg_task.hand_init_pos_noise, device=self.device) + above_fixed_pos_rand = above_fixed_pos_rand @ torch.diag(hand_init_pos_rand) + above_fixed_pos[bad_envs] += above_fixed_pos_rand + + # (b) get random orientation facing down + hand_down_euler = torch.tensor( + self.cfg_task.hand_init_orn, device=self.device + ).unsqueeze(0).repeat(n_bad, 1) + + rand_sample = torch.rand((n_bad, 3), dtype=torch.float32, device=self.device) + above_fixed_orn_noise = 2 * (rand_sample - 0.5) # [-1, 1] + hand_init_orn_rand = torch.tensor(self.cfg_task.hand_init_orn_noise, device=self.device) + above_fixed_orn_noise = above_fixed_orn_noise @ torch.diag(hand_init_orn_rand) + hand_down_euler += above_fixed_orn_noise + self.hand_down_euler[bad_envs, ...] = hand_down_euler + hand_down_quat[bad_envs, :] = torch_utils.quat_from_euler_xyz( + roll=hand_down_euler[:, 0], + pitch=hand_down_euler[:, 1], + yaw=hand_down_euler[:, 2]) + + # (c) iterative IK Method + self.ctrl_target_fingertip_midpoint_pos[bad_envs, ...] = above_fixed_pos[bad_envs, ...] + self.ctrl_target_fingertip_midpoint_quat[bad_envs, ...] = hand_down_quat[bad_envs, :] + + pos_error, aa_error = self.set_pos_inverse_kinematics(env_ids=bad_envs) + pos_error = torch.linalg.norm(pos_error, dim=1) > 1e-3 + angle_error = torch.norm(aa_error, dim=1) > 1e-3 + any_error = torch.logical_or(pos_error, angle_error) + bad_envs = bad_envs[any_error.nonzero(as_tuple=False).squeeze(-1)] + + if bad_envs.shape[0] == 0: + print('Done IK') + break + + self._set_franka_to_default_pose( + joints=[0.00871, -0.10368, -0.00794, -1.49139, -0.00083, 1.38774, 0.0], + env_ids=bad_envs) + + ik_attempt += 1 + print(f'IK Attempt: {ik_attempt}\tBad Envs: {bad_envs.shape[0]}') + + self.step_sim_no_action() + + # Add flanking gears after servo (so arm doesn't move them). + if self.cfg_task.name == 'gear_meshing' and self.cfg_task.add_flanking_gears: + small_gear_state = self._small_gear_asset.data.default_root_state.clone()[env_ids] + small_gear_state[:, 0:7] = fixed_state[:, 0:7] + small_gear_state[:, 7:] = 0.0 # vel + self._small_gear_asset.write_root_state_to_sim(small_gear_state, env_ids=env_ids) + self._small_gear_asset.reset() + + large_gear_state = self._large_gear_asset.data.default_root_state.clone()[env_ids] + large_gear_state[:, 0:7] = fixed_state[:, 0:7] + large_gear_state[:, 7:] = 0.0 # vel + self._large_gear_asset.write_root_state_to_sim(large_gear_state, env_ids=env_ids) + self._large_gear_asset.reset() + + # (3) Randomize asset-in-gripper location. + # flip gripper z orientation + flip_z_quat = torch.tensor( + [0.0, 0.0, 1.0, 0.0], device=self.device + ).unsqueeze(0).repeat(self.num_envs, 1) + fingertip_flipped_quat, fingertip_flipped_pos = torch_utils.tf_combine( + q1=self.fingertip_midpoint_quat, + t1=self.fingertip_midpoint_pos, + q2=flip_z_quat, + t2=torch.zeros_like(self.fingertip_midpoint_pos)) + + # get default gripper in asset transform + held_asset_relative_pos, held_asset_relative_quat = self.get_handheld_asset_relative_pose() + asset_in_hand_quat, asset_in_hand_pos = torch_utils.tf_inverse( + held_asset_relative_quat, + held_asset_relative_pos) + + translated_held_asset_quat, translated_held_asset_pos = torch_utils.tf_combine( + q1=fingertip_flipped_quat, + t1=fingertip_flipped_pos, + q2=asset_in_hand_quat, + t2=asset_in_hand_pos) + + # Add asset in hand randomization + rand_sample = torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device) + self.held_asset_pos_noise = 2 * (rand_sample - 0.5) # [-1, 1] + if self.cfg_task.name == 'gear_meshing': + self.held_asset_pos_noise[:, 2] = -rand_sample[:, 2] # [-1, 0] + + held_asset_pos_noise = torch.tensor(self.cfg_task.held_asset_pos_noise, device=self.device) + self.held_asset_pos_noise = self.held_asset_pos_noise @ torch.diag(held_asset_pos_noise) + translated_held_asset_quat, translated_held_asset_pos = torch_utils.tf_combine( + q1=translated_held_asset_quat, + t1=translated_held_asset_pos, + q2=self.identity_quat, + t2=self.held_asset_pos_noise) + + held_state = self._held_asset.data.default_root_state.clone() + held_state[:, 0:3] = translated_held_asset_pos + self.scene.env_origins + held_state[:, 3:7] = translated_held_asset_quat + held_state[:, 7:] = 0.0 + self._held_asset.write_root_state_to_sim(held_state) + self._held_asset.reset() + + # Close hand + # Set gains to use for quick resets. + reset_task_prop_gains = torch.tensor( + self.cfg.ctrl.reset_task_prop_gains, + device=self.device + ).repeat((self.num_envs, 1)) + reset_rot_deriv_scale = self.cfg.ctrl.reset_rot_deriv_scale + self._set_gains(reset_task_prop_gains, reset_rot_deriv_scale) + + self.step_sim_no_action() + + grasp_time = 0.0 + while grasp_time < 0.25: + self.ctrl_target_joint_pos[env_ids, 7:] = 0.0 # Close gripper. + self.ctrl_target_gripper_dof_pos = 0.0 + self.close_gripper_in_place() + self.step_sim_no_action() + grasp_time += self.sim.get_physics_dt() + + diff = self.target_held_base_pos - self.held_base_pos + + bad_idxs = env_ids[diff[:, 1] > 0.05] + print('Bad:', bad_idxs) + + self.prev_joint_pos = self.joint_pos[:, 0:7].clone() + self.prev_fingertip_pos = self.fingertip_midpoint_pos.clone() + self.prev_fingertip_quat = self.fingertip_midpoint_quat.clone() + + # Set initial actions to involve no-movement. Needed for EMA/correct penalties. + self.actions = torch.zeros_like(self.actions) + self.prev_actions = torch.zeros_like(self.actions) + # Back out what actions should be for initial state. + # Relative position to bolt tip. + self.fixed_pos_action_frame[:] = self.fixed_pos_obs_frame + self.init_fixed_pos_obs_noise + + pos_actions = self.fingertip_midpoint_pos - self.fixed_pos_action_frame + pos_action_bounds = torch.tensor(self.cfg.ctrl.pos_action_bounds, device=self.device) + pos_actions = pos_actions @ torch.diag(1./pos_action_bounds) + self.actions[:, 0:3] = self.prev_actions[:, 0:3] = pos_actions + + # Relative yaw to bolt. + unrot_180_euler = torch.tensor( + [-np.pi, 0., 0.], device=self.device + ).repeat(self.num_envs, 1) + unrot_quat = torch_utils.quat_from_euler_xyz( + roll=unrot_180_euler[:, 0], + pitch=unrot_180_euler[:, 1], + yaw=unrot_180_euler[:, 2]) + + fingertip_quat_rel_bolt = torch_utils.quat_mul(unrot_quat, self.fingertip_midpoint_quat) + fingertip_yaw_bolt = torch_utils.get_euler_xyz(fingertip_quat_rel_bolt)[-1] + fingertip_yaw_bolt = torch.where( + fingertip_yaw_bolt > torch.pi / 2, + fingertip_yaw_bolt - 2 * torch.pi, + fingertip_yaw_bolt + ) + fingertip_yaw_bolt = torch.where( + fingertip_yaw_bolt < -torch.pi, + fingertip_yaw_bolt + 2 * torch.pi, + fingertip_yaw_bolt + ) + + yaw_action = (fingertip_yaw_bolt + np.deg2rad(180.)) / np.deg2rad(270.) * 2. - 1. + self.actions[:, 5] = self.prev_actions[:, 5] = yaw_action + + # Zero initial velocity. + self.ee_angvel_fd[:, :] = 0.0 + self.ee_linvel_fd[:, :] = 0.0 + + # Set initial gains for the episode. + self._set_gains(self.default_gains) + + physics_sim_view.set_gravity(carb.Float3(*self.cfg.sim.gravity)) + print('Done Reset') diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py new file mode 100644 index 0000000000..ad05a28e85 --- /dev/null +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py @@ -0,0 +1,215 @@ +import omni.isaac.lab.sim as sim_utils + +from omni.isaac.lab.actuators.actuator_cfg import ImplicitActuatorCfg +from omni.isaac.lab.assets import ArticulationCfg, RigidObjectCfg +from omni.isaac.lab.envs import DirectRLEnvCfg +from omni.isaac.lab.scene import InteractiveSceneCfg +from omni.isaac.lab.sim import PhysxCfg, SimulationCfg +from omni.isaac.lab.utils import configclass +from omni.isaac.lab.sim.spawners.materials.physics_materials_cfg import RigidBodyMaterialCfg + +from .factory_tasks_cfg import FactoryTask, PegInsertion, GearMeshing, NutThreading, ASSET_DIR + +OBS_DIM_CFG = { + 'fingertip_pos': 3, + 'fingertip_pos_rel_fixed': 3, + 'fingertip_quat': 4, + 'ee_linvel': 3, + 'ee_angvel': 3, +} + +STATE_DIM_CFG = { + 'fingertip_pos': 3, + 'fingertip_pos_rel_fixed': 3, + 'fingertip_quat': 4, + 'ee_linvel': 3, + 'ee_angvel': 3, + 'joint_pos': 7, + 'held_pos': 3, + 'held_pos_rel_fixed': 3, + 'held_quat': 4, + 'fixed_pos': 3, + 'fixed_quat': 4, + 'task_prop_gains': 6, + 'ema_factor': 1, + 'pos_threshold': 3, + 'rot_threshold': 3 +} + +@configclass +class ObsRandCfg: + fixed_asset_pos = [0.001, 0.001, 0.001] + +@configclass +class CtrlCfg: + ema_factor = 0.2 + + pos_action_bounds = [0.05, 0.05, 0.05] + rot_action_bounds = [1.0, 1.0, 1.0] + + pos_action_threshold = [0.02, 0.02, 0.02] + rot_action_threshold = [0.097, 0.097, 0.097] + + reset_task_prop_gains = [300, 300, 300, 20, 20, 20] + reset_rot_deriv_scale = 10.0 + default_task_prop_gains = [100, 100, 100, 30, 30, 30] + + default_dof_pos_tensor = [-1.3003, -0.4015, 1.1791, -2.1493, 0.4001, 1.9425, 0.4754] + kp_null = 10.0 + kd_null = 6.3246 + +@configclass +class FactoryEnvCfg(DirectRLEnvCfg): + decimation = 8 + action_space = 6 + # num_*: will be overwritten to correspond to obs_order, state_order. + observation_space = 21 + state_space = 72 + obs_order: list = [ + 'fingertip_pos_rel_fixed', + 'fingertip_quat', + 'ee_linvel', + 'ee_angvel'] + state_order: list = [ + 'fingertip_pos', + 'fingertip_quat', + 'ee_linvel', + 'ee_angvel', + 'joint_pos', + 'held_pos', + 'held_pos_rel_fixed', + 'held_quat', + 'fixed_pos', + 'fixed_quat'] + + task_name: str = 'peg_insertion' # peg_insertion, gear_meshing, nut_threading + tasks: dict = { + 'peg_insertion': PegInsertion(), + 'gear_meshing': GearMeshing(), + 'nut_threading': NutThreading() + } + # task: FactoryTask = PegInsertion() # PegInsertion, GearMeshing, NutThreading + obs_rand: ObsRandCfg = ObsRandCfg() + ctrl: CtrlCfg = CtrlCfg() + + episode_length_s = 10.0 # Probably need to override. + sim: SimulationCfg = SimulationCfg( + device="cuda:0", + dt=1/120, + gravity=(0.0, 0.0, -9.81), + physx=PhysxCfg( + solver_type=1, + max_position_iteration_count=192, # Important to avoid interpenetration. + max_velocity_iteration_count=1, + bounce_threshold_velocity=0.2, + friction_offset_threshold=0.01, + friction_correlation_distance=0.00625, + gpu_max_rigid_contact_count=2**23, + gpu_max_rigid_patch_count=2**23, + gpu_max_num_partitions=1 # Important for stable simulation. + ), + physics_material=RigidBodyMaterialCfg( + static_friction=1.0, + dynamic_friction=1.0, + ), + ) + + scene: InteractiveSceneCfg = InteractiveSceneCfg( + num_envs=128, + env_spacing=1.0 + ) + + robot = ArticulationCfg( + prim_path="/World/envs/env_.*/Robot", + spawn=sim_utils.UsdFileCfg( + usd_path=f'{ASSET_DIR}/franka_mimic.usd', + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + articulation_props=sim_utils.ArticulationRootPropertiesCfg( + enabled_self_collisions=False, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + ), + collision_props=sim_utils.CollisionPropertiesCfg( + contact_offset=0.005, + rest_offset=0.0 + ), + ), + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + "panda_joint1": 0.00871, + "panda_joint2": -0.10368, + "panda_joint3": -0.00794, + "panda_joint4": -1.49139, + "panda_joint5": -0.00083, + "panda_joint6": 1.38774, + "panda_joint7": 0.0, + "panda_finger_joint2": 0.04, + }, + pos=(0.0, 0.0, 0.0), + rot=(1.0, 0.0, 0.0, 0.0), + ), + actuators={ + "panda_arm1": ImplicitActuatorCfg( + joint_names_expr=["panda_joint[1-4]"], + stiffness=0.0, + damping=0.0, + friction=0.0, + armature=0.0, + effort_limit=87, + velocity_limit=124.6 + ), + "panda_arm2": ImplicitActuatorCfg( + joint_names_expr=["panda_joint[5-7]"], + stiffness=0.0, + damping=0.0, + friction=0.0, + armature=0.0, + effort_limit=12, + velocity_limit=149.5 + ), + "panda_hand": ImplicitActuatorCfg( + joint_names_expr=["panda_finger_joint[1-2]"], + effort_limit=40.0, + velocity_limit=0.04, + stiffness=7500.0, + damping=173.0, + friction=0.1, + armature=0.0, + ), + }, + ) + + table: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/Table", + spawn=sim_utils.UsdFileCfg( + usd_path=f'{ASSET_DIR}/Table/table.usd', + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + ), + init_state=RigidObjectCfg.InitialStateCfg( + pos=(0.5, 0.0, -0.2), + rot=(1.0, 0.0, 0.0, 0.0), + ) + ) diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py new file mode 100644 index 0000000000..b794399f0c --- /dev/null +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py @@ -0,0 +1,457 @@ +from omni.isaac.lab.utils import configclass +from omni.isaac.lab.assets import RigidObjectCfg +import omni.isaac.lab.sim as sim_utils + +ASSET_DIR = '../IsaacLab_benchmark/source/extensions/omni.isaac.lab_assets/data/Factory' + +@configclass +class FixedAssetCfg: + usd_path: str = '' + diameter: float = 0.0 + height: float = 0.0 + base_height: float = 0.0 # Used to compute held asset CoM. + friction: float = 0.75 + mass: float = 0.05 + +@configclass +class HeldAssetCfg: + usd_path: str = '' + diameter: float = 0.0 # Used for gripper width. + height: float = 0.0 + friction: float = 0.75 + mass: float = 0.05 + +@configclass +class RobotCfg: + robot_usd: str = '' + franka_fingerpad_length: float = 0.017608 + friction: float = 0.75 + +@configclass +class FactoryTask: + robot_cfg: RobotCfg = RobotCfg() + name: str = '' + duration_s = 5.0 + + fixed_asset_cfg: FixedAssetCfg = FixedAssetCfg() + held_asset_cfg: HeldAssetCfg = HeldAssetCfg() + asset_size: float = 0.0 + + # Robot + hand_init_pos: list = [0.0, 0.0, 0.015] # Relative to fixed asset tip. + hand_init_pos_noise: list = [0.02, 0.02, 0.01] + hand_init_orn: list = [3.1416, 0, 2.356] + hand_init_orn_noise: list = [0., 0., 1.57] + + # Action + unidirectional_rot: bool = False + + # Fixed Asset (applies to all tasks) + fixed_asset_init_pos_noise: list = [0.05, 0.05, 0.05] + fixed_asset_init_orn_deg: float = 0.0 + fixed_asset_init_orn_range_deg: float = 360.0 + + # Held Asset (applies to all tasks) + held_asset_pos_noise: list = [0.0, 0.006, 0.003] # noise level of the held asset in gripper + held_asset_rot_init: float = -90.0 + + # Reward + ee_success_yaw: float = 0.0 # nut_threading task only. + action_penalty_scale: float = 0.0 + action_grad_penalty_scale: float = 0.0 + # Reward function details can be found in Appendix B of https://arxiv.org/pdf/2408.04587. + # Multi-scale keypoints are used to capture different phases of the task. + # Each reward passes the keypoint distance, x, through a squashing function: + # r(x) = 1/(exp(-ax) + b + exp(ax)). + # Each list defines [a, b] which control the slope and maximum of the squashing function. + num_keypoints: int = 4 + keypoint_scale: float = 0.15 + keypoint_coef_baseline: list = [5, 4] # General movement towards fixed object. + keypoint_coef_coarse: list = [50, 2] # Movement to align the assets. + keypoint_coef_fine: list = [100, 0] # Smaller distances for threading or last-inch insertion. + # Fixed-asset height fraction for which different bonuses are rewarded (see individual tasks). + success_threshold: float = 0.04 + engage_threshold: float = 0.9 + + +@configclass +class Peg8mm(HeldAssetCfg): + usd_path = f'{ASSET_DIR}/factory_peg_8mm.usd' + diameter = 0.007986 + height = 0.050 + mass = 0.019 + +@configclass +class Hole8mm(FixedAssetCfg): + usd_path = f'{ASSET_DIR}/factory_hole_8mm.usd' + diameter = 0.0081 + height = 0.025 + base_height = 0.0 + +@configclass +class PegInsertion(FactoryTask): + name = 'peg_insertion' + fixed_asset_cfg = Hole8mm() + held_asset_cfg = Peg8mm() + asset_size = 8.0 + duration_s = 10.0 + + # Robot + hand_init_pos: list = [0.0, 0.0, 0.047] # Relative to fixed asset tip. + hand_init_pos_noise: list = [0.02, 0.02, 0.01] + hand_init_orn: list = [3.1416, 0.0, 0.0] + hand_init_orn_noise: list = [0.0, 0.0, 0.785] + + # Fixed Asset (applies to all tasks) + fixed_asset_init_pos_noise: list = [0.05, 0.05, 0.05] + fixed_asset_init_orn_deg: float = 0.0 + fixed_asset_init_orn_range_deg: float = 360.0 + + # Held Asset (applies to all tasks) + held_asset_pos_noise: list = [0.003, 0.0, 0.003] # noise level of the held asset in gripper + held_asset_rot_init: float = 0.0 + + # Rewards + keypoint_coef_baseline: list = [5, 4] + keypoint_coef_coarse: list = [50, 2] + keypoint_coef_fine: list = [100, 0] + # Fraction of socket height. + success_threshold: float = 0.04 + engage_threshold: float = 0.9 + + fixed_asset: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/FixedAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=fixed_asset_cfg.usd_path, + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=fixed_asset_cfg.mass), + collision_props=sim_utils.CollisionPropertiesCfg( + contact_offset=0.005, + rest_offset=0.0 + ), + ), + init_state=RigidObjectCfg.InitialStateCfg( + pos=(0.6, 0.0, 0.05), + rot=(1.0, 0.0, 0.0, 0.0), + ) + ) + held_asset: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/HeldAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=held_asset_cfg.usd_path, + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=held_asset_cfg.mass), + collision_props=sim_utils.CollisionPropertiesCfg( + contact_offset=0.005, + rest_offset=0.0 + ), + ), + init_state=RigidObjectCfg.InitialStateCfg( + pos=(0.0, 0.4, 0.1), + rot=(1.0, 0.0, 0.0, 0.0), + ) + ) + + + +@configclass +class GearBase(FixedAssetCfg): + usd_path = f'{ASSET_DIR}/factory_gear_base.usd' + height = 0.02 + base_height = 0.005 + small_gear_base_offset = [5.075e-2, 0.0, 0.0] + medium_gear_base_offset = [2.025e-2, 0.0, 0.0] + large_gear_base_offset = [-3.025e-2, 0.0, 0.0] + +@configclass +class MediumGear(HeldAssetCfg): + usd_path = f'{ASSET_DIR}/factory_gear_medium.usd' + diameter = 0.03 # Used for gripper width. + height: float = 0.03 + mass = 0.012 + + +@configclass +class GearMeshing(FactoryTask): + name = 'gear_meshing' + fixed_asset_cfg = GearBase() + held_asset_cfg = MediumGear() + target_gear = 'gear_medium' + duration_s = 20.0 + + small_gear_usd = f'{ASSET_DIR}/factory_gear_small.usd' + large_gear_usd = f'{ASSET_DIR}/factory_gear_large.usd' + + small_gear_cfg: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/SmallGearAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=small_gear_usd, + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=0.019), + collision_props=sim_utils.CollisionPropertiesCfg( + contact_offset=0.005, + rest_offset=0.0 + ), + ), + init_state=RigidObjectCfg.InitialStateCfg( + pos=(0.0, 0.4, 0.1), + rot=(1.0, 0.0, 0.0, 0.0), + ) + ) + + large_gear_cfg: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/LargeGearAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=large_gear_usd, + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=0.019), + collision_props=sim_utils.CollisionPropertiesCfg( + contact_offset=0.005, + rest_offset=0.0 + ), + ), + init_state=RigidObjectCfg.InitialStateCfg( + pos=(0.0, 0.4, 0.1), + rot=(1.0, 0.0, 0.0, 0.0), + ) + ) + + # Gears Asset + add_flanking_gears = True + add_flanking_gears_prob = 1.0 + + # Robot + hand_init_pos: list = [0.0, 0.0, 0.035] # Relative to fixed asset tip. + hand_init_pos_noise: list = [0.02, 0.02, 0.01] + hand_init_orn: list = [3.1416, 0, 0.0] + hand_init_orn_noise: list = [0., 0., 0.785] + + # Fixed Asset (applies to all tasks) + fixed_asset_init_pos_noise: list = [0.05, 0.05, 0.05] + fixed_asset_init_orn_deg: float = 0.0 + fixed_asset_init_orn_range_deg: float = 15.0 + + # Held Asset (applies to all tasks) + held_asset_pos_noise: list = [0.003, 0.0, 0.003] # noise level of the held asset in gripper + held_asset_rot_init: float = -90.0 + + keypoint_coef_baseline: list = [5, 4] + keypoint_coef_coarse: list = [50, 2] + keypoint_coef_fine: list = [100, 0] + # Fraction of gear peg height. + success_threshold: float = 0.05 + engage_threshold: float = 0.9 + + fixed_asset: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/FixedAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=fixed_asset_cfg.usd_path, + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=fixed_asset_cfg.mass), + collision_props=sim_utils.CollisionPropertiesCfg( + contact_offset=0.005, + rest_offset=0.0 + ), + ), + init_state=RigidObjectCfg.InitialStateCfg( + pos=(0.6, 0.0, 0.05), + rot=(1.0, 0.0, 0.0, 0.0), + ) + ) + held_asset: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/HeldAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=held_asset_cfg.usd_path, + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=held_asset_cfg.mass), + collision_props=sim_utils.CollisionPropertiesCfg( + contact_offset=0.005, + rest_offset=0.0 + ), + ), + init_state=RigidObjectCfg.InitialStateCfg( + pos=(0.0, 0.4, 0.1), + rot=(1.0, 0.0, 0.0, 0.0), + ) + ) + +@configclass +class NutM16(HeldAssetCfg): + usd_path = f'{ASSET_DIR}/factory_nut_m16.usd' + diameter = 0.024 + height = 0.01 + mass = 0.03 + friction = 0.01 # Additive with the nut means friction is (-0.25 + 0.75)/2 = 0.25 + +@configclass +class BoltM16(FixedAssetCfg): + usd_path = f'{ASSET_DIR}/factory_bolt_m16.usd' + diameter = 0.024 + height = 0.025 + base_height = 0.01 + thread_pitch = 0.002 + +@configclass +class NutThreading(FactoryTask): + name = 'nut_threading' + fixed_asset_cfg = BoltM16() + held_asset_cfg = NutM16() + asset_size = 16.0 + duration_s = 30.0 + + # Robot + hand_init_pos: list = [0.0, 0.0, 0.015] # Relative to fixed asset tip. + hand_init_pos_noise: list = [0.02, 0.02, 0.01] + hand_init_orn: list = [3.1416, 0.0, 1.83] + hand_init_orn_noise: list = [0.0, 0.0, 0.26] + + # Action + unidirectional_rot: bool = True + + # Fixed Asset (applies to all tasks) + fixed_asset_init_pos_noise: list = [0.05, 0.05, 0.05] + fixed_asset_init_orn_deg: float = 120.0 + fixed_asset_init_orn_range_deg: float = 30.0 + + # Held Asset (applies to all tasks) + held_asset_pos_noise: list = [0.0, 0.003, 0.003] # noise level of the held asset in gripper + held_asset_rot_init: float = -90.0 + + # Reward. + ee_success_yaw = 0.0 + keypoint_coef_baseline: list = [100, 2] + keypoint_coef_coarse: list = [500, 2] # 100, 2 + keypoint_coef_fine: list = [1500, 0] # 500, 0 + # Fraction of thread-height. + success_threshold: float = 0.375 + engage_threshold: float = 0.5 + keypoint_scale: float = 0.05 + + fixed_asset: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/FixedAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=fixed_asset_cfg.usd_path, + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=False, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=fixed_asset_cfg.mass), + collision_props=sim_utils.CollisionPropertiesCfg( + contact_offset=0.005, + rest_offset=0.0 + ), + ), + init_state=RigidObjectCfg.InitialStateCfg( + pos=(0.6, 0.0, 0.05), + rot=(1.0, 0.0, 0.0, 0.0), + ) + ) + held_asset: RigidObjectCfg = RigidObjectCfg( + prim_path="/World/envs/env_.*/HeldAsset", + spawn=sim_utils.UsdFileCfg( + usd_path=held_asset_cfg.usd_path, + activate_contact_sensors=True, + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=True, + max_depenetration_velocity=5.0, + linear_damping=0.0, + angular_damping=0.0, + max_linear_velocity=1000.0, + max_angular_velocity=3666.0, + enable_gyroscopic_forces=True, + solver_position_iteration_count=192, + solver_velocity_iteration_count=1, + max_contact_impulse=1e32, + ), + mass_props=sim_utils.MassPropertiesCfg(mass=held_asset_cfg.mass), + collision_props=sim_utils.CollisionPropertiesCfg( + contact_offset=0.005, + rest_offset=0.0 + ), + ), + init_state=RigidObjectCfg.InitialStateCfg( + pos=(0.0, 0.4, 0.1), + rot=(1.0, 0.0, 0.0, 0.0), + ) + ) diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/torch_jit_utils.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/torch_jit_utils.py new file mode 100644 index 0000000000..484641ec61 --- /dev/null +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/torch_jit_utils.py @@ -0,0 +1,61 @@ +# Copyright (c) 2018-2022, NVIDIA Corporation +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# 3. Neither the name of the copyright holder nor the names of its +# contributors may be used to endorse or promote products derived from +# this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import torch +import omni.isaac.core.utils.torch as torch_utils + + +""" +Rotation conversions +""" + +@torch.jit.script +def quat_to_angle_axis(q): + # type: (Tensor) -> Tuple[Tensor, Tensor] + # computes axis-angle representation from quaternion q + # q must be normalized + min_theta = 1e-5 + qx, qy, qz, qw = 1, 2, 3, 0 + + sin_theta = torch.sqrt(1 - q[..., qw] * q[..., qw]) + angle = 2 * torch.acos(q[..., qw]) + angle = torch_utils.normalize_angle(angle) + sin_theta_expand = sin_theta.unsqueeze(-1) + axis = q[..., qx:] / sin_theta_expand + + mask = sin_theta > min_theta + default_axis = torch.zeros_like(axis) + default_axis[..., -1] = 1 + + angle = torch.where(mask, angle, torch.zeros_like(angle)) + mask_expand = mask.unsqueeze(-1) + axis = torch.where(mask_expand, axis, default_axis) + return angle, axis + + +# EOF diff --git a/source/standalone/workflows/rl_games/play.py b/source/standalone/workflows/rl_games/play.py index d16e46b2ef..7aa1456fe0 100644 --- a/source/standalone/workflows/rl_games/play.py +++ b/source/standalone/workflows/rl_games/play.py @@ -155,7 +155,7 @@ def main(): # convert obs to agent format obs = agent.obs_to_torch(obs) # agent stepping - actions = agent.get_action(obs, is_deterministic=True) + actions = agent.get_action(obs, is_deterministic=agent.is_deterministic) # env stepping obs, _, dones, _ = env.step(actions) From e33bea0e4eaf687f916fa41a4a09311c69dca6e5 Mon Sep 17 00:00:00 2001 From: Michael Noseworthy Date: Thu, 5 Dec 2024 15:14:47 -0500 Subject: [PATCH 02/15] Use table already in omniverse --- .../lab_tasks/direct/factory/factory_env.py | 8 +++-- .../direct/factory/factory_env_cfg.py | 29 ++----------------- 2 files changed, 9 insertions(+), 28 deletions(-) diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py index 78a86110ed..6a04316795 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py @@ -7,6 +7,7 @@ from omni.isaac.lab.assets import Articulation, RigidObject from omni.isaac.lab.envs import DirectRLEnv from omni.isaac.lab.sim.spawners.from_files import GroundPlaneCfg, spawn_ground_plane +from omni.isaac.lab.utils.assets import ISAAC_NUCLEUS_DIR from . import factory_control as fc @@ -172,8 +173,12 @@ def _setup_scene(self): cfg=GroundPlaneCfg(), translation=(0.0, 0.0, -0.4) ) + + # spawn a usd file of a table into the scene + cfg = sim_utils.UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd") + cfg.func("/World/envs/env_.*/Table", cfg, translation=(0.55, 0.0, 0.0), orientation=(0.70711, 0.0, 0.0, 0.70711)) + self._robot = Articulation(self.cfg.robot) - self._table = RigidObject(self.cfg.table) self._fixed_asset = RigidObject(self.cfg_task.fixed_asset) self._held_asset = RigidObject(self.cfg_task.held_asset) if self.cfg_task.name == 'gear_meshing': @@ -184,7 +189,6 @@ def _setup_scene(self): self.scene.filter_collisions() self.scene.articulations["robot"] = self._robot - self.scene.rigid_objects["table"] = self._table self.scene.rigid_objects["fixed_asset"] = self._fixed_asset self.scene.rigid_objects["held_asset"] = self._held_asset if self.cfg_task.name == 'gear_meshing': diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py index ad05a28e85..db2ac3b643 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py @@ -1,14 +1,14 @@ import omni.isaac.lab.sim as sim_utils from omni.isaac.lab.actuators.actuator_cfg import ImplicitActuatorCfg -from omni.isaac.lab.assets import ArticulationCfg, RigidObjectCfg +from omni.isaac.lab.assets import ArticulationCfg from omni.isaac.lab.envs import DirectRLEnvCfg from omni.isaac.lab.scene import InteractiveSceneCfg from omni.isaac.lab.sim import PhysxCfg, SimulationCfg from omni.isaac.lab.utils import configclass from omni.isaac.lab.sim.spawners.materials.physics_materials_cfg import RigidBodyMaterialCfg -from .factory_tasks_cfg import FactoryTask, PegInsertion, GearMeshing, NutThreading, ASSET_DIR +from .factory_tasks_cfg import PegInsertion, GearMeshing, NutThreading, ASSET_DIR OBS_DIM_CFG = { 'fingertip_pos': 3, @@ -116,7 +116,7 @@ class FactoryEnvCfg(DirectRLEnvCfg): scene: InteractiveSceneCfg = InteractiveSceneCfg( num_envs=128, - env_spacing=1.0 + env_spacing=2.0 ) robot = ArticulationCfg( @@ -190,26 +190,3 @@ class FactoryEnvCfg(DirectRLEnvCfg): ), }, ) - - table: RigidObjectCfg = RigidObjectCfg( - prim_path="/World/envs/env_.*/Table", - spawn=sim_utils.UsdFileCfg( - usd_path=f'{ASSET_DIR}/Table/table.usd', - rigid_props=sim_utils.RigidBodyPropertiesCfg( - disable_gravity=True, - max_depenetration_velocity=5.0, - linear_damping=0.0, - angular_damping=0.0, - max_linear_velocity=1000.0, - max_angular_velocity=3666.0, - enable_gyroscopic_forces=True, - solver_position_iteration_count=192, - solver_velocity_iteration_count=1, - max_contact_impulse=1e32, - ), - ), - init_state=RigidObjectCfg.InitialStateCfg( - pos=(0.5, 0.0, -0.2), - rot=(1.0, 0.0, 0.0, 0.0), - ) - ) From b40062fe0b128819bc97a129a4c541e679b3cac6 Mon Sep 17 00:00:00 2001 From: Michael Noseworthy Date: Sun, 8 Dec 2024 12:24:51 -0500 Subject: [PATCH 03/15] Change assets to articulations --- .../lab_tasks/direct/factory/factory_env.py | 18 ++--- .../direct/factory/factory_tasks_cfg.py | 77 ++++++++++++------- 2 files changed, 59 insertions(+), 36 deletions(-) diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py index 6a04316795..76f1923d50 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py @@ -4,7 +4,7 @@ import carb import omni.isaac.core.utils.torch as torch_utils import omni.isaac.lab.sim as sim_utils -from omni.isaac.lab.assets import Articulation, RigidObject +from omni.isaac.lab.assets import Articulation from omni.isaac.lab.envs import DirectRLEnv from omni.isaac.lab.sim.spawners.from_files import GroundPlaneCfg, spawn_ground_plane from omni.isaac.lab.utils.assets import ISAAC_NUCLEUS_DIR @@ -179,21 +179,21 @@ def _setup_scene(self): cfg.func("/World/envs/env_.*/Table", cfg, translation=(0.55, 0.0, 0.0), orientation=(0.70711, 0.0, 0.0, 0.70711)) self._robot = Articulation(self.cfg.robot) - self._fixed_asset = RigidObject(self.cfg_task.fixed_asset) - self._held_asset = RigidObject(self.cfg_task.held_asset) + self._fixed_asset = Articulation(self.cfg_task.fixed_asset) + self._held_asset = Articulation(self.cfg_task.held_asset) if self.cfg_task.name == 'gear_meshing': - self._small_gear_asset = RigidObject(self.cfg_task.small_gear_cfg) - self._large_gear_asset = RigidObject(self.cfg_task.large_gear_cfg) + self._small_gear_asset = Articulation(self.cfg_task.small_gear_cfg) + self._large_gear_asset = Articulation(self.cfg_task.large_gear_cfg) self.scene.clone_environments(copy_from_source=False) self.scene.filter_collisions() self.scene.articulations["robot"] = self._robot - self.scene.rigid_objects["fixed_asset"] = self._fixed_asset - self.scene.rigid_objects["held_asset"] = self._held_asset + self.scene.articulations["fixed_asset"] = self._fixed_asset + self.scene.articulations["held_asset"] = self._held_asset if self.cfg_task.name == 'gear_meshing': - self.scene.rigid_objects["small_gear"] = self._small_gear_asset - self.scene.rigid_objects["large_gear"] = self._large_gear_asset + self.scene.articulations["small_gear"] = self._small_gear_asset + self.scene.articulations["large_gear"] = self._large_gear_asset # add lights light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75)) diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py index b794399f0c..2c411ce585 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py @@ -1,5 +1,6 @@ from omni.isaac.lab.utils import configclass -from omni.isaac.lab.assets import RigidObjectCfg +from omni.isaac.lab.assets import ArticulationCfg + import omni.isaac.lab.sim as sim_utils ASSET_DIR = '../IsaacLab_benchmark/source/extensions/omni.isaac.lab_assets/data/Factory' @@ -119,7 +120,7 @@ class PegInsertion(FactoryTask): success_threshold: float = 0.04 engage_threshold: float = 0.9 - fixed_asset: RigidObjectCfg = RigidObjectCfg( + fixed_asset: ArticulationCfg = ArticulationCfg( prim_path="/World/envs/env_.*/FixedAsset", spawn=sim_utils.UsdFileCfg( usd_path=fixed_asset_cfg.usd_path, @@ -142,12 +143,15 @@ class PegInsertion(FactoryTask): rest_offset=0.0 ), ), - init_state=RigidObjectCfg.InitialStateCfg( + init_state=ArticulationCfg.InitialStateCfg( pos=(0.6, 0.0, 0.05), rot=(1.0, 0.0, 0.0, 0.0), - ) + joint_pos={}, + joint_vel={} + ), + actuators={} ) - held_asset: RigidObjectCfg = RigidObjectCfg( + held_asset: ArticulationCfg = ArticulationCfg( prim_path="/World/envs/env_.*/HeldAsset", spawn=sim_utils.UsdFileCfg( usd_path=held_asset_cfg.usd_path, @@ -170,14 +174,15 @@ class PegInsertion(FactoryTask): rest_offset=0.0 ), ), - init_state=RigidObjectCfg.InitialStateCfg( + init_state=ArticulationCfg.InitialStateCfg( pos=(0.0, 0.4, 0.1), rot=(1.0, 0.0, 0.0, 0.0), - ) + joint_pos={}, + joint_vel={} + ), + actuators={} ) - - @configclass class GearBase(FixedAssetCfg): usd_path = f'{ASSET_DIR}/factory_gear_base.usd' @@ -206,7 +211,7 @@ class GearMeshing(FactoryTask): small_gear_usd = f'{ASSET_DIR}/factory_gear_small.usd' large_gear_usd = f'{ASSET_DIR}/factory_gear_large.usd' - small_gear_cfg: RigidObjectCfg = RigidObjectCfg( + small_gear_cfg: ArticulationCfg = ArticulationCfg( prim_path="/World/envs/env_.*/SmallGearAsset", spawn=sim_utils.UsdFileCfg( usd_path=small_gear_usd, @@ -229,13 +234,16 @@ class GearMeshing(FactoryTask): rest_offset=0.0 ), ), - init_state=RigidObjectCfg.InitialStateCfg( + init_state=ArticulationCfg.InitialStateCfg( pos=(0.0, 0.4, 0.1), rot=(1.0, 0.0, 0.0, 0.0), - ) + joint_pos={}, + joint_vel={} + ), + actuators={} ) - large_gear_cfg: RigidObjectCfg = RigidObjectCfg( + large_gear_cfg: ArticulationCfg = ArticulationCfg( prim_path="/World/envs/env_.*/LargeGearAsset", spawn=sim_utils.UsdFileCfg( usd_path=large_gear_usd, @@ -258,10 +266,13 @@ class GearMeshing(FactoryTask): rest_offset=0.0 ), ), - init_state=RigidObjectCfg.InitialStateCfg( + init_state=ArticulationCfg.InitialStateCfg( pos=(0.0, 0.4, 0.1), rot=(1.0, 0.0, 0.0, 0.0), - ) + joint_pos={}, + joint_vel={} + ), + actuators={} ) # Gears Asset @@ -290,7 +301,7 @@ class GearMeshing(FactoryTask): success_threshold: float = 0.05 engage_threshold: float = 0.9 - fixed_asset: RigidObjectCfg = RigidObjectCfg( + fixed_asset: ArticulationCfg = ArticulationCfg( prim_path="/World/envs/env_.*/FixedAsset", spawn=sim_utils.UsdFileCfg( usd_path=fixed_asset_cfg.usd_path, @@ -313,12 +324,15 @@ class GearMeshing(FactoryTask): rest_offset=0.0 ), ), - init_state=RigidObjectCfg.InitialStateCfg( + init_state=ArticulationCfg.InitialStateCfg( pos=(0.6, 0.0, 0.05), rot=(1.0, 0.0, 0.0, 0.0), - ) + joint_pos={}, + joint_vel={} + ), + actuators={} ) - held_asset: RigidObjectCfg = RigidObjectCfg( + held_asset: ArticulationCfg = ArticulationCfg( prim_path="/World/envs/env_.*/HeldAsset", spawn=sim_utils.UsdFileCfg( usd_path=held_asset_cfg.usd_path, @@ -341,10 +355,13 @@ class GearMeshing(FactoryTask): rest_offset=0.0 ), ), - init_state=RigidObjectCfg.InitialStateCfg( + init_state=ArticulationCfg.InitialStateCfg( pos=(0.0, 0.4, 0.1), rot=(1.0, 0.0, 0.0, 0.0), - ) + joint_pos={}, + joint_vel={} + ), + actuators={} ) @configclass @@ -399,7 +416,7 @@ class NutThreading(FactoryTask): engage_threshold: float = 0.5 keypoint_scale: float = 0.05 - fixed_asset: RigidObjectCfg = RigidObjectCfg( + fixed_asset: ArticulationCfg = ArticulationCfg( prim_path="/World/envs/env_.*/FixedAsset", spawn=sim_utils.UsdFileCfg( usd_path=fixed_asset_cfg.usd_path, @@ -422,12 +439,15 @@ class NutThreading(FactoryTask): rest_offset=0.0 ), ), - init_state=RigidObjectCfg.InitialStateCfg( + init_state=ArticulationCfg.InitialStateCfg( pos=(0.6, 0.0, 0.05), rot=(1.0, 0.0, 0.0, 0.0), - ) + joint_pos={}, + joint_vel={} + ), + actuators={} ) - held_asset: RigidObjectCfg = RigidObjectCfg( + held_asset: ArticulationCfg = ArticulationCfg( prim_path="/World/envs/env_.*/HeldAsset", spawn=sim_utils.UsdFileCfg( usd_path=held_asset_cfg.usd_path, @@ -450,8 +470,11 @@ class NutThreading(FactoryTask): rest_offset=0.0 ), ), - init_state=RigidObjectCfg.InitialStateCfg( + init_state=ArticulationCfg.InitialStateCfg( pos=(0.0, 0.4, 0.1), rot=(1.0, 0.0, 0.0, 0.0), - ) + joint_pos={}, + joint_vel={} + ), + actuators={} ) From fe76be3a901a10cb33953611b9d1f51a9023e16a Mon Sep 17 00:00:00 2001 From: Iretiayo Akinola Date: Sun, 8 Dec 2024 23:24:53 -0800 Subject: [PATCH 04/15] adding contact-rich environments (from Factory) to documentation --- .../_static/tasks/factory/gear_meshing.png | Bin 0 -> 586548 bytes .../tasks/factory/nut_bolt_fastening.png | Bin 0 -> 435793 bytes .../_static/tasks/factory/peg_insertion.png | Bin 0 -> 444028 bytes docs/source/overview/environments.rst | 33 ++++++++++++++++++ 4 files changed, 33 insertions(+) create mode 100644 docs/source/_static/tasks/factory/gear_meshing.png create mode 100644 docs/source/_static/tasks/factory/nut_bolt_fastening.png create mode 100644 docs/source/_static/tasks/factory/peg_insertion.png diff --git a/docs/source/_static/tasks/factory/gear_meshing.png b/docs/source/_static/tasks/factory/gear_meshing.png new file mode 100644 index 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zaK%+1fCx#lvFk+YDF;)Wn|h5lzB2zoF09xLpq~@1T6@ayh!;U9%$MkIC zF3?5i-CgeT%V-nix0-0C9G|Ws6qkf`|0zAUzTVXW?;l@bp58K&sov%_!Yz!`hfKQj znmiZ3RH2h<@>EP=bho6%6f6n_2LX&Y!ny&%Q=BBkgwz~Y*^!cE)alAN6j7Ra@`%9f>_Cg!$1H$ zE=SYYTg7}9{7k5(uY3Tccz>m iMsQ+JTpjfXi8uUQegwHY1Tx`__ .. |cube-shadow-vis-link| replace:: `Isaac-Repose-Cube-Shadow-Vision-Direct-v0 `__ +Contact-rich Manipulation +~~~~~~~~~~~~ + +Environments based on contact-rich manipulation tasks such as peg insertion, gear meshing and nut-bolt fastening. + +These tasks share the same task configurations and control options. You can switch between them by specifying the task name. +For example: + +* |factory-peg-link|: Peg insertion with the Franka arm +* |factory-gear-link|: Gear meshing with the Franka arm +* |factory-nut-link|: Nut-Bolt fastening with the Franka arm + +.. table:: + :widths: 33 37 30 + + +--------------------+-------------------------+-----------------------------------------------------------------------------+ + | World | Environment ID | Description | + +====================+=========================+=============================================================================+ + | |factory-peg| | |factory-peg-link| | Insert peg into the socket with the Franka robot | + +--------------------+-------------------------+-----------------------------------------------------------------------------+ + | |factory-gear| | |factory-gear-link| | Insert and mesh gear into the base with other gears, using the Franka robot | + +--------------------+-------------------------+-----------------------------------------------------------------------------+ + | |factory-nut| | |factory-nut-link| | Thread the nut onto the first 2 threads of the bolt, using the Franka robot | + +--------------------+-------------------------+-----------------------------------------------------------------------------+ + +.. |factory-peg| image:: ../_static/tasks/factory/peg_insertion.png +.. |factory-gear| image:: ../_static/tasks/factory/gear_meshing.png +.. |factory-nut| image:: ../_static/tasks/factory/nut_bolt_fastening.png + +.. |factory-peg-link| replace:: `Factory-Direct-v0 env.task_name=peg_insertion `__ +.. |factory-gear-link| replace:: `Factory-Direct-v0 env.task_name=gear_meshing `__ +.. |factory-nut-link| replace:: `Factory-Direct-v0 env.task_name=nut_threading `__ + Locomotion ~~~~~~~~~~ From a287a99b28f8cdf8b7a524b478b2852fa84a25c6 Mon Sep 17 00:00:00 2001 From: Michael Noseworthy Date: Mon, 9 Dec 2024 17:49:45 -0500 Subject: [PATCH 05/15] Run pre-commit formatting --- .../lab_tasks/direct/factory/__init__.py | 7 +- .../direct/factory/agents/__init__.py | 4 + .../factory/agents/rl_games_ppo_cfg.yaml | 7 +- .../direct/factory/factory_control.py | 249 +++++---- .../lab_tasks/direct/factory/factory_env.py | 488 ++++++++---------- .../direct/factory/factory_env_cfg.py | 122 +++-- .../direct/factory/factory_tasks_cfg.py | 159 +++--- .../direct/factory/torch_jit_utils.py | 32 +- 8 files changed, 486 insertions(+), 582 deletions(-) diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/__init__.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/__init__.py index fe255eab5f..3c0e92dd39 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/__init__.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/__init__.py @@ -1,3 +1,8 @@ +# Copyright (c) 2022-2024, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + import gymnasium as gym from . import agents @@ -13,6 +18,6 @@ disable_env_checker=True, kwargs={ "env_cfg_entry_point": FactoryEnvCfg, - "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml" + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", }, ) diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/agents/__init__.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/agents/__init__.py index e69de29bb2..c3ee657052 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/agents/__init__.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2024, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/agents/rl_games_ppo_cfg.yaml b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/agents/rl_games_ppo_cfg.yaml index 4f580cc2e7..9c31db0854 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/agents/rl_games_ppo_cfg.yaml +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/agents/rl_games_ppo_cfg.yaml @@ -32,7 +32,7 @@ params: name: default regularizer: name: None - + rnn: name: lstm units: 1024 @@ -95,7 +95,7 @@ params: network: name: actor_critic central_value: True - + mlp: units: [512, 128, 64] activation: elu @@ -105,7 +105,7 @@ params: name: default regularizer: name: None - + rnn: name: lstm units: 1024 @@ -115,4 +115,3 @@ params: layer_norm: True player: deterministic: False - diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_control.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_control.py index cf3e331c9a..3b6165927c 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_control.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_control.py @@ -1,30 +1,7 @@ -# Copyright (c) 2021-2022, NVIDIA Corporation +# Copyright (c) 2022-2024, The Isaac Lab Project Developers. # All rights reserved. # -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# 1. Redistributions of source code must retain the above copyright notice, this -# list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# -# 3. Neither the name of the copyright holder nor the names of its -# contributors may be used to endorse or promote products derived from -# this software without specific prior written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +# SPDX-License-Identifier: BSD-3-Clause """Factory: control module. @@ -37,32 +14,34 @@ import omni.isaac.core.utils.torch as torch_utils -def compute_dof_pos_target(cfg_ctrl, - arm_dof_pos, - fingertip_midpoint_pos, - fingertip_midpoint_quat, - jacobian, - ctrl_target_fingertip_midpoint_pos, - ctrl_target_fingertip_midpoint_quat, - ctrl_target_gripper_dof_pos, - device): +def compute_dof_pos_target( + cfg_ctrl, + arm_dof_pos, + fingertip_midpoint_pos, + fingertip_midpoint_quat, + jacobian, + ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat, + ctrl_target_gripper_dof_pos, + device, +): """Compute Franka DOF position target to move fingertips towards target pose.""" - ctrl_target_dof_pos = torch.zeros((cfg_ctrl['num_envs'], 9), device=device) + ctrl_target_dof_pos = torch.zeros((cfg_ctrl["num_envs"], 9), device=device) pos_error, axis_angle_error = get_pose_error( fingertip_midpoint_pos=fingertip_midpoint_pos, fingertip_midpoint_quat=fingertip_midpoint_quat, ctrl_target_fingertip_midpoint_pos=ctrl_target_fingertip_midpoint_pos, ctrl_target_fingertip_midpoint_quat=ctrl_target_fingertip_midpoint_quat, - jacobian_type=cfg_ctrl['jacobian_type'], - rot_error_type='axis_angle') + jacobian_type=cfg_ctrl["jacobian_type"], + rot_error_type="axis_angle", + ) delta_fingertip_pose = torch.cat((pos_error, axis_angle_error), dim=1) - delta_arm_dof_pos = _get_delta_dof_pos(delta_pose=delta_fingertip_pose, - ik_method=cfg_ctrl['ik_method'], - jacobian=jacobian, - device=device) + delta_arm_dof_pos = _get_delta_dof_pos( + delta_pose=delta_fingertip_pose, ik_method=cfg_ctrl["ik_method"], jacobian=jacobian, device=device + ) ctrl_target_dof_pos[:, 0:7] = arm_dof_pos + delta_arm_dof_pos ctrl_target_dof_pos[:, 7:9] = ctrl_target_gripper_dof_pos # gripper finger joints @@ -70,20 +49,22 @@ def compute_dof_pos_target(cfg_ctrl, return ctrl_target_dof_pos -def compute_dof_torque(cfg, - dof_pos, - dof_vel, - fingertip_midpoint_pos, - fingertip_midpoint_quat, - fingertip_midpoint_linvel, - fingertip_midpoint_angvel, - jacobian, - arm_mass_matrix, - ctrl_target_fingertip_midpoint_pos, - ctrl_target_fingertip_midpoint_quat, - task_prop_gains, - task_deriv_gains, - device): +def compute_dof_torque( + cfg, + dof_pos, + dof_vel, + fingertip_midpoint_pos, + fingertip_midpoint_quat, + fingertip_midpoint_linvel, + fingertip_midpoint_angvel, + jacobian, + arm_mass_matrix, + ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat, + task_prop_gains, + task_deriv_gains, + device, +): """Compute Franka DOF torque to move fingertips towards target pose.""" # References: # 1) https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf @@ -98,8 +79,9 @@ def compute_dof_torque(cfg, fingertip_midpoint_quat=fingertip_midpoint_quat, ctrl_target_fingertip_midpoint_pos=ctrl_target_fingertip_midpoint_pos, ctrl_target_fingertip_midpoint_quat=ctrl_target_fingertip_midpoint_quat, - jacobian_type='geometric', - rot_error_type='axis_angle') + jacobian_type="geometric", + rot_error_type="axis_angle", + ) delta_fingertip_pose = torch.cat((pos_error, axis_angle_error), dim=1) # Set tau = k_p * task_pos_error - k_d * task_vel_error (building towards eq. 3.96-3.98) @@ -108,7 +90,7 @@ def compute_dof_torque(cfg, fingertip_midpoint_linvel=fingertip_midpoint_linvel, fingertip_midpoint_angvel=fingertip_midpoint_angvel, task_prop_gains=task_prop_gains, - task_deriv_gains=task_deriv_gains + task_deriv_gains=task_deriv_gains, ) task_wrench += task_wrench_motion @@ -123,15 +105,15 @@ def compute_dof_torque(cfg, arm_mass_matrix_inv = torch.inverse(arm_mass_matrix) jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2) arm_mass_matrix_task = torch.inverse( - jacobian @ torch.inverse(arm_mass_matrix) @ jacobian_T) # ETH eq. 3.86; geometric Jacobian is assumed + jacobian @ torch.inverse(arm_mass_matrix) @ jacobian_T + ) # ETH eq. 3.86; geometric Jacobian is assumed j_eef_inv = arm_mass_matrix_task @ jacobian @ arm_mass_matrix_inv - default_dof_pos_tensor = torch.tensor( - cfg.ctrl.default_dof_pos_tensor, - device=device - ).repeat((num_envs, 1)) + default_dof_pos_tensor = torch.tensor(cfg.ctrl.default_dof_pos_tensor, device=device).repeat((num_envs, 1)) # nullspace computation distance_to_default_dof_pos = default_dof_pos_tensor - dof_pos[:, :7] - distance_to_default_dof_pos = ((distance_to_default_dof_pos + math.pi) % (2 * math.pi) - math.pi) # normalize to [-pi, pi] + distance_to_default_dof_pos = (distance_to_default_dof_pos + math.pi) % ( + 2 * math.pi + ) - math.pi # normalize to [-pi, pi] u_null = cfg.ctrl.kd_null * -dof_vel[:, :7] + cfg.ctrl.kp_null * distance_to_default_dof_pos u_null = arm_mass_matrix @ u_null.unsqueeze(-1) torque_null = (torch.eye(7, device=device).unsqueeze(0) - torch.transpose(jacobian, 1, 2) @ j_eef_inv) @ u_null @@ -142,12 +124,14 @@ def compute_dof_torque(cfg, return dof_torque, task_wrench -def get_pose_error(fingertip_midpoint_pos, - fingertip_midpoint_quat, - ctrl_target_fingertip_midpoint_pos, - ctrl_target_fingertip_midpoint_quat, - jacobian_type, - rot_error_type): +def get_pose_error( + fingertip_midpoint_pos, + fingertip_midpoint_quat, + ctrl_target_fingertip_midpoint_pos, + ctrl_target_fingertip_midpoint_quat, + jacobian_type, + rot_error_type, +): """Compute task-space error between target Franka fingertip pose and current pose.""" # Reference: https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf @@ -155,43 +139,42 @@ def get_pose_error(fingertip_midpoint_pos, pos_error = ctrl_target_fingertip_midpoint_pos - fingertip_midpoint_pos # Compute rot error - if jacobian_type == 'geometric': # See example 2.9.8; note use of J_g and transformation between rotation vectors + if jacobian_type == "geometric": # See example 2.9.8; note use of J_g and transformation between rotation vectors # Compute quat error (i.e., difference quat) # Reference: https://personal.utdallas.edu/~sxb027100/dock/quat.html # Check for shortest path using quaternion dot product. - quat_dot = (ctrl_target_fingertip_midpoint_quat*fingertip_midpoint_quat).sum(dim=1, keepdim=True) + quat_dot = (ctrl_target_fingertip_midpoint_quat * fingertip_midpoint_quat).sum(dim=1, keepdim=True) ctrl_target_fingertip_midpoint_quat = torch.where( - quat_dot.expand(-1, 4) >= 0, - ctrl_target_fingertip_midpoint_quat, - -ctrl_target_fingertip_midpoint_quat + quat_dot.expand(-1, 4) >= 0, ctrl_target_fingertip_midpoint_quat, -ctrl_target_fingertip_midpoint_quat ) - fingertip_midpoint_quat_norm = torch_utils.quat_mul(fingertip_midpoint_quat, - torch_utils.quat_conjugate(fingertip_midpoint_quat))[:, 0] # scalar component + fingertip_midpoint_quat_norm = torch_utils.quat_mul( + fingertip_midpoint_quat, torch_utils.quat_conjugate(fingertip_midpoint_quat) + )[ + :, 0 + ] # scalar component fingertip_midpoint_quat_inv = torch_utils.quat_conjugate( - fingertip_midpoint_quat) / fingertip_midpoint_quat_norm.unsqueeze(-1) + fingertip_midpoint_quat + ) / fingertip_midpoint_quat_norm.unsqueeze(-1) quat_error = torch_utils.quat_mul(ctrl_target_fingertip_midpoint_quat, fingertip_midpoint_quat_inv) # Convert to axis-angle error axis_angle_error = axis_angle_from_quat(quat_error) - elif jacobian_type == 'analytic': # See example 2.9.7; note use of J_a and difference of rotation vectors + elif jacobian_type == "analytic": # See example 2.9.7; note use of J_a and difference of rotation vectors # Compute axis-angle error - axis_angle_error = axis_angle_from_quat(ctrl_target_fingertip_midpoint_quat)\ - - axis_angle_from_quat(fingertip_midpoint_quat) + axis_angle_error = axis_angle_from_quat(ctrl_target_fingertip_midpoint_quat) - axis_angle_from_quat( + fingertip_midpoint_quat + ) - if rot_error_type == 'quat': + if rot_error_type == "quat": return pos_error, quat_error - elif rot_error_type == 'axis_angle': + elif rot_error_type == "axis_angle": return pos_error, axis_angle_error -def _get_wrench_error(left_finger_force, - right_finger_force, - ctrl_target_fingertip_contact_wrench, - num_envs, - device): +def _get_wrench_error(left_finger_force, right_finger_force, ctrl_target_fingertip_contact_wrench, num_envs, device): """Compute task-space error between target Franka fingertip contact wrench and current wrench.""" fingertip_contact_wrench = torch.zeros((num_envs, 6), device=device) @@ -211,56 +194,58 @@ def _get_delta_dof_pos(delta_pose, ik_method, jacobian, device): # 1) https://www.cs.cmu.edu/~15464-s13/lectures/lecture6/iksurvey.pdf # 2) https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf (p. 47) - if ik_method == 'pinv': # Jacobian pseudoinverse + if ik_method == "pinv": # Jacobian pseudoinverse k_val = 1.0 jacobian_pinv = torch.linalg.pinv(jacobian) delta_dof_pos = k_val * jacobian_pinv @ delta_pose.unsqueeze(-1) delta_dof_pos = delta_dof_pos.squeeze(-1) - elif ik_method == 'trans': # Jacobian transpose + elif ik_method == "trans": # Jacobian transpose k_val = 1.0 jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2) delta_dof_pos = k_val * jacobian_T @ delta_pose.unsqueeze(-1) delta_dof_pos = delta_dof_pos.squeeze(-1) - elif ik_method == 'dls': # damped least squares (Levenberg-Marquardt) + elif ik_method == "dls": # damped least squares (Levenberg-Marquardt) lambda_val = 0.1 # 0.1 jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2) - lambda_matrix = (lambda_val ** 2) * torch.eye(n=jacobian.shape[1], device=device) + lambda_matrix = (lambda_val**2) * torch.eye(n=jacobian.shape[1], device=device) delta_dof_pos = jacobian_T @ torch.inverse(jacobian @ jacobian_T + lambda_matrix) @ delta_pose.unsqueeze(-1) delta_dof_pos = delta_dof_pos.squeeze(-1) - elif ik_method == 'svd': # adaptive SVD + elif ik_method == "svd": # adaptive SVD k_val = 1.0 U, S, Vh = torch.linalg.svd(jacobian) - S_inv = 1. / S + S_inv = 1.0 / S min_singular_value = 1.0e-5 S_inv = torch.where(S > min_singular_value, S_inv, torch.zeros_like(S_inv)) - jacobian_pinv = torch.transpose(Vh, dim0=1, dim1=2)[:, :, :6] @ torch.diag_embed(S_inv) @ torch.transpose(U, dim0=1, dim1=2) + jacobian_pinv = ( + torch.transpose(Vh, dim0=1, dim1=2)[:, :, :6] @ torch.diag_embed(S_inv) @ torch.transpose(U, dim0=1, dim1=2) + ) delta_dof_pos = k_val * jacobian_pinv @ delta_pose.unsqueeze(-1) delta_dof_pos = delta_dof_pos.squeeze(-1) return delta_dof_pos -def _apply_task_space_gains(delta_fingertip_pose, - fingertip_midpoint_linvel, - fingertip_midpoint_angvel, - task_prop_gains, - task_deriv_gains): +def _apply_task_space_gains( + delta_fingertip_pose, fingertip_midpoint_linvel, fingertip_midpoint_angvel, task_prop_gains, task_deriv_gains +): """Interpret PD gains as task-space gains. Apply to task-space error.""" task_wrench = torch.zeros_like(delta_fingertip_pose) # Apply gains to lin error components lin_error = delta_fingertip_pose[:, 0:3] - task_wrench[:, 0:3] = task_prop_gains[:, 0:3] * lin_error + \ - task_deriv_gains[:, 0:3] * (0.0 - fingertip_midpoint_linvel) + task_wrench[:, 0:3] = task_prop_gains[:, 0:3] * lin_error + task_deriv_gains[:, 0:3] * ( + 0.0 - fingertip_midpoint_linvel + ) # Apply gains to rot error components rot_error = delta_fingertip_pose[:, 3:6] - task_wrench[:, 3:6] = task_prop_gains[:, 3:6] * rot_error + \ - task_deriv_gains[:, 3:6] * (0.0 - fingertip_midpoint_angvel) + task_wrench[:, 3:6] = task_prop_gains[:, 3:6] * rot_error + task_deriv_gains[:, 3:6] * ( + 0.0 - fingertip_midpoint_angvel + ) return task_wrench @@ -279,23 +264,28 @@ def get_analytic_jacobian(fingertip_quat, fingertip_jacobian, num_envs, device): # Eq. 2.107 gives an expression for E_r_inv # Compute E_inv_top (i.e., [E_p_inv, 0]) - I = torch.eye(3, device=device) - E_p_inv = I.repeat((batch, 1)).reshape(batch, 3, 3) + eye = torch.eye(3, device=device) + E_p_inv = eye.repeat((batch, 1)).reshape(batch, 3, 3) E_inv_top = torch.cat((E_p_inv, torch.zeros((batch, 3, 3), device=device)), dim=2) # Compute E_inv_bottom (i.e., [0, E_r_inv]) fingertip_axis_angle = axis_angle_from_quat(fingertip_quat) fingertip_axis_angle_cross = get_skew_symm_matrix(fingertip_axis_angle, device=device) fingertip_angle = torch.linalg.vector_norm(fingertip_axis_angle, dim=1) - factor_1 = 1 / (fingertip_angle ** 2) + factor_1 = 1 / (fingertip_angle**2) factor_2 = 1 - fingertip_angle * 0.5 * torch.sin(fingertip_angle) / (1 - torch.cos(fingertip_angle)) factor_3 = factor_1 * factor_2 - E_r_inv = I \ - - 1 * 0.5 * fingertip_axis_angle_cross \ - + (fingertip_axis_angle_cross @ fingertip_axis_angle_cross) * factor_3.unsqueeze(-1).repeat((1, 3 * 3)).reshape((batch, 3, 3)) + E_r_inv = ( + eye + - 1 * 0.5 * fingertip_axis_angle_cross + + (fingertip_axis_angle_cross @ fingertip_axis_angle_cross) + * factor_3.unsqueeze(-1).repeat((1, 3 * 3)).reshape((batch, 3, 3)) + ) E_inv_bottom = torch.cat((torch.zeros((batch, 3, 3), device=device), E_r_inv), dim=2) - E_inv = torch.cat((E_inv_top.reshape((batch, 3 * 6)), E_inv_bottom.reshape((batch, 3 * 6))), dim=1).reshape((batch, 6, 6)) + E_inv = torch.cat((E_inv_top.reshape((batch, 3 * 6)), E_inv_bottom.reshape((batch, 3 * 6))), dim=1).reshape( + (batch, 6, 6) + ) J_a = E_inv @ fingertip_jacobian @@ -307,12 +297,12 @@ def get_skew_symm_matrix(vec, device): # Reference: https://en.wikipedia.org/wiki/Cross_product#Conversion_to_matrix_multiplication batch = vec.shape[0] - I = torch.eye(3, device=device) - skew_symm = torch.transpose(torch.cross(vec.repeat((1, 3)).reshape((batch * 3, 3)), - I.repeat((batch, 1))) - .reshape(batch, 3, 3), - dim0=1, - dim1=2) + eye = torch.eye(3, device=device) + skew_symm = torch.transpose( + torch.cross(vec.repeat((1, 3)).reshape((batch * 3, 3)), eye.repeat((batch, 1))).reshape(batch, 3, 3), + dim0=1, + dim1=2, + ) return skew_symm @@ -322,10 +312,9 @@ def translate_along_local_z(pos, quat, offset, device): num_vecs = pos.shape[0] offset_vec = offset * torch.tensor([0.0, 0.0, 1.0], device=device).repeat((num_vecs, 1)) - _, translated_pos = torch_utils.tf_combine(q1=quat, - t1=pos, - q2=torch.tensor([1.0, 0.0, 0.0, 0.0], device=device).repeat((num_vecs, 1)), - t2=offset_vec) + _, translated_pos = torch_utils.tf_combine( + q1=quat, t1=pos, q2=torch.tensor([1.0, 0.0, 0.0, 0.0], device=device).repeat((num_vecs, 1)), t2=offset_vec + ) return translated_pos @@ -334,10 +323,9 @@ def translate_along_local_xyz(pos, quat, offset_vec, device): """Translate global body position along local frame and express in global coordinates.""" num_vecs = pos.shape[0] - _, translated_pos = torch_utils.tf_combine(q1=quat, - t1=pos, - q2=torch.tensor([1.0, 0.0, 0.0, 0.0], device=device).repeat((num_vecs, 1)), - t2=offset_vec) + _, translated_pos = torch_utils.tf_combine( + q1=quat, t1=pos, q2=torch.tensor([1.0, 0.0, 0.0, 0.0], device=device).repeat((num_vecs, 1)), t2=offset_vec + ) return translated_pos @@ -346,7 +334,7 @@ def axis_angle_from_euler(euler): """Convert tensor of Euler angles to tensor of axis-angles.""" quat = torch_utils.quat_from_euler_xyz(roll=euler[:, 0], pitch=euler[:, 1], yaw=euler[:, 2]) - quat = quat * torch.sign(quat[:, 0]).unsqueeze(-1) # smaller rotation + quat = quat * torch.sign(quat[:, 0]).unsqueeze(-1) # smaller rotation axis_angle = axis_angle_from_quat(quat) return axis_angle @@ -359,9 +347,9 @@ def axis_angle_from_quat(quat, eps=1.0e-6): mag = torch.linalg.norm(quat[:, 1:], dim=1) half_angle = torch.atan2(mag, quat[:, 0]) angle = 2.0 * half_angle - sin_half_angle_over_angle = torch.where(torch.abs(angle) > eps, - torch.sin(half_angle) / angle, - 1. / 2. - angle ** 2.0 / 48) + sin_half_angle_over_angle = torch.where( + torch.abs(angle) > eps, torch.sin(half_angle) / angle, 1.0 / 2.0 - angle**2.0 / 48 + ) axis_angle = quat[:, 1:] / sin_half_angle_over_angle.unsqueeze(-1) @@ -401,6 +389,7 @@ def get_nonrand_quat(num_quats, rot_perturbation, device): quat = torch_utils.quat_from_euler_xyz( torch.rand((num_quats, 1), device=device).squeeze() * rot_perturbation * 2.0 - rot_perturbation, torch.rand((num_quats, 1), device=device).squeeze() * rot_perturbation * 2.0 - rot_perturbation, - torch.rand((num_quats, 1), device=device).squeeze() * rot_perturbation * 2.0 - rot_perturbation) + torch.rand((num_quats, 1), device=device).squeeze() * rot_perturbation * 2.0 - rot_perturbation, + ) return quat diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py index 76f1923d50..69c01bb2aa 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py @@ -1,17 +1,22 @@ +# Copyright (c) 2022-2024, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + import numpy as np import torch import carb import omni.isaac.core.utils.torch as torch_utils + import omni.isaac.lab.sim as sim_utils from omni.isaac.lab.assets import Articulation from omni.isaac.lab.envs import DirectRLEnv from omni.isaac.lab.sim.spawners.from_files import GroundPlaneCfg, spawn_ground_plane from omni.isaac.lab.utils.assets import ISAAC_NUCLEUS_DIR - from . import factory_control as fc -from .factory_env_cfg import FactoryEnvCfg, OBS_DIM_CFG, STATE_DIM_CFG +from .factory_env_cfg import OBS_DIM_CFG, STATE_DIM_CFG, FactoryEnvCfg from .torch_jit_utils import quat_to_angle_axis @@ -34,7 +39,7 @@ def __init__(self, cfg: FactoryEnvCfg, render_mode: str | None = None, **kwargs) self._compute_intermediate_values(dt=self.physics_dt) def _set_body_inertias(self): - """ Note: this is to account for the asset_options.armature parameter in IGE. """ + """Note: this is to account for the asset_options.armature parameter in IGE.""" inertias = self._robot.root_physx_view.get_inertias() offset = torch.zeros_like(inertias) offset[:, :, [0, 4, 8]] += 0.01 @@ -42,20 +47,17 @@ def _set_body_inertias(self): self._robot.root_physx_view.set_inertias(new_inertias, torch.arange(self.num_envs)) def _set_default_dynamics_parameters(self): - """ Set parameters defining dynamic interactions. """ - self.default_gains = torch.tensor( - self.cfg.ctrl.default_task_prop_gains, - device=self.device - ).repeat((self.num_envs, 1)) - - self.pos_threshold = torch.tensor( - self.cfg.ctrl.pos_action_threshold, - device=self.device - ).repeat((self.num_envs, 1)) - self.rot_threshold = torch.tensor( - self.cfg.ctrl.rot_action_threshold, - device=self.device - ).repeat((self.num_envs, 1)) + """Set parameters defining dynamic interactions.""" + self.default_gains = torch.tensor(self.cfg.ctrl.default_task_prop_gains, device=self.device).repeat( + (self.num_envs, 1) + ) + + self.pos_threshold = torch.tensor(self.cfg.ctrl.pos_action_threshold, device=self.device).repeat( + (self.num_envs, 1) + ) + self.rot_threshold = torch.tensor(self.cfg.ctrl.rot_action_threshold, device=self.device).repeat( + (self.num_envs, 1) + ) # Set masses and frictions. self._set_friction(self._held_asset, self.cfg_task.held_asset_cfg.friction) @@ -63,7 +65,7 @@ def _set_default_dynamics_parameters(self): self._set_friction(self._robot, self.cfg_task.robot_cfg.friction) def _set_friction(self, asset, value): - """ Update material properties for a given asset. """ + """Update material properties for a given asset.""" materials = asset.root_physx_view.get_material_properties() materials[..., 0] = value # Static friction. materials[..., 1] = value # Dynamic friction. @@ -71,43 +73,35 @@ def _set_friction(self, asset, value): asset.root_physx_view.set_material_properties(materials, env_ids) def _init_tensors(self): - """ Initialize tensors once. """ - self.identity_quat = torch.tensor( - [1.0, 0.0, 0.0, 0.0], device=self.device - ).unsqueeze(0).repeat(self.num_envs, 1) + """Initialize tensors once.""" + self.identity_quat = ( + torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).unsqueeze(0).repeat(self.num_envs, 1) + ) # Control targets. - self.ctrl_target_joint_pos = torch.zeros( - (self.num_envs, self._robot.num_joints), device=self.device) - self.ctrl_target_fingertip_midpoint_pos = torch.zeros( - (self.num_envs, 3), device=self.device) - self.ctrl_target_fingertip_midpoint_quat = torch.zeros( - (self.num_envs, 4), device=self.device) + self.ctrl_target_joint_pos = torch.zeros((self.num_envs, self._robot.num_joints), device=self.device) + self.ctrl_target_fingertip_midpoint_pos = torch.zeros((self.num_envs, 3), device=self.device) + self.ctrl_target_fingertip_midpoint_quat = torch.zeros((self.num_envs, 4), device=self.device) # Fixed asset. - self.fixed_pos_action_frame = torch.zeros( - (self.num_envs, 3), device=self.device) - self.fixed_pos_obs_frame = torch.zeros( - (self.num_envs, 3), device=self.device) - self.init_fixed_pos_obs_noise = torch.zeros( - (self.num_envs, 3), device=self.device) + self.fixed_pos_action_frame = torch.zeros((self.num_envs, 3), device=self.device) + self.fixed_pos_obs_frame = torch.zeros((self.num_envs, 3), device=self.device) + self.init_fixed_pos_obs_noise = torch.zeros((self.num_envs, 3), device=self.device) # Held asset held_base_x_offset = 0.0 - if self.cfg_task.name == 'peg_insertion': + if self.cfg_task.name == "peg_insertion": held_base_z_offset = 0.0 - elif self.cfg_task.name == 'gear_meshing': + elif self.cfg_task.name == "gear_meshing": gear_base_offset = self._get_target_gear_base_offset() held_base_x_offset = gear_base_offset[0] held_base_z_offset = gear_base_offset[2] - elif self.cfg_task.name == 'nut_threading': + elif self.cfg_task.name == "nut_threading": held_base_z_offset = self.cfg_task.fixed_asset_cfg.base_height else: - raise NotImplementedError('Task not implemented') + raise NotImplementedError("Task not implemented") - self.held_base_pos_local = torch.tensor( - [0.0, 0.0, 0.0], device=self.device - ).repeat((self.num_envs, 1)) + self.held_base_pos_local = torch.tensor([0.0, 0.0, 0.0], device=self.device).repeat((self.num_envs, 1)) self.held_base_pos_local[:, 0] = held_base_x_offset self.held_base_pos_local[:, 2] = held_base_z_offset self.held_base_quat_local = self.identity_quat.clone().detach() @@ -116,72 +110,65 @@ def _init_tensors(self): self.held_base_quat = self.identity_quat.clone().detach() # Computer body indices. - self.left_finger_body_idx = self._robot.body_names.index('panda_leftfinger') - self.right_finger_body_idx = self._robot.body_names.index('panda_rightfinger') - self.fingertip_body_idx = self._robot.body_names.index('panda_fingertip_centered') + self.left_finger_body_idx = self._robot.body_names.index("panda_leftfinger") + self.right_finger_body_idx = self._robot.body_names.index("panda_rightfinger") + self.fingertip_body_idx = self._robot.body_names.index("panda_fingertip_centered") # Tensors for finite-differencing. self.last_update_timestamp = 0.0 # Note: This is for finite differencing body velocities. - self.prev_fingertip_pos = torch.zeros( - (self.num_envs, 3), device=self.device) + self.prev_fingertip_pos = torch.zeros((self.num_envs, 3), device=self.device) self.prev_fingertip_quat = self.identity_quat.clone() - self.prev_joint_pos = torch.zeros( - (self.num_envs, 7), device=self.device) + self.prev_joint_pos = torch.zeros((self.num_envs, 7), device=self.device) # Keypoint tensors. - self.target_held_base_pos = torch.zeros( - (self.num_envs, 3), device=self.device) + self.target_held_base_pos = torch.zeros((self.num_envs, 3), device=self.device) self.target_held_base_quat = self.identity_quat.clone().detach() offsets = self._get_keypoint_offsets(self.cfg_task.num_keypoints) self.keypoint_offsets = offsets * self.cfg_task.keypoint_scale - self.keypoints_held = torch.zeros( - (self.num_envs, self.cfg_task.num_keypoints, 3),device=self.device) + self.keypoints_held = torch.zeros((self.num_envs, self.cfg_task.num_keypoints, 3), device=self.device) self.keypoints_fixed = torch.zeros_like(self.keypoints_held, device=self.device) # Used to compute target poses. - self.fixed_success_pos_local = torch.zeros( - (self.num_envs, 3), device=self.device) - if self.cfg_task.name == 'peg_insertion': + self.fixed_success_pos_local = torch.zeros((self.num_envs, 3), device=self.device) + if self.cfg_task.name == "peg_insertion": self.fixed_success_pos_local[:, 2] = 0.0 - elif self.cfg_task.name == 'gear_meshing': + elif self.cfg_task.name == "gear_meshing": gear_base_offset = self._get_target_gear_base_offset() self.fixed_success_pos_local[:, 0] = gear_base_offset[0] self.fixed_success_pos_local[:, 2] = gear_base_offset[2] - elif self.cfg_task.name == 'nut_threading': + elif self.cfg_task.name == "nut_threading": head_height = self.cfg_task.fixed_asset_cfg.base_height shank_length = self.cfg_task.fixed_asset_cfg.height - thread_pitch = self.cfg_task.fixed_asset_cfg.thread_pitch + thread_pitch = self.cfg_task.fixed_asset_cfg.thread_pitch self.fixed_success_pos_local[:, 2] = head_height + shank_length - thread_pitch * 1.5 else: - raise NotImplementedError('Task not implemented') + raise NotImplementedError("Task not implemented") self.ep_succeeded = torch.zeros((self.num_envs,), dtype=torch.long, device=self.device) self.ep_success_times = torch.zeros((self.num_envs,), dtype=torch.long, device=self.device) def _get_keypoint_offsets(self, num_keypoints): - """ Get uniformly-spaced keypoints along a line of unit length, centered at 0. """ + """Get uniformly-spaced keypoints along a line of unit length, centered at 0.""" keypoint_offsets = torch.zeros((num_keypoints, 3), device=self.device) keypoint_offsets[:, -1] = torch.linspace(0.0, 1.0, num_keypoints, device=self.device) - 0.5 return keypoint_offsets def _setup_scene(self): - """ Initialize simulation scene. """ - spawn_ground_plane( - prim_path="/World/ground", - cfg=GroundPlaneCfg(), - translation=(0.0, 0.0, -0.4) - ) + """Initialize simulation scene.""" + spawn_ground_plane(prim_path="/World/ground", cfg=GroundPlaneCfg(), translation=(0.0, 0.0, -0.4)) # spawn a usd file of a table into the scene cfg = sim_utils.UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd") - cfg.func("/World/envs/env_.*/Table", cfg, translation=(0.55, 0.0, 0.0), orientation=(0.70711, 0.0, 0.0, 0.70711)) + cfg.func( + "/World/envs/env_.*/Table", cfg, translation=(0.55, 0.0, 0.0), orientation=(0.70711, 0.0, 0.0, 0.70711) + ) self._robot = Articulation(self.cfg.robot) self._fixed_asset = Articulation(self.cfg_task.fixed_asset) self._held_asset = Articulation(self.cfg_task.held_asset) - if self.cfg_task.name == 'gear_meshing': + if self.cfg_task.name == "gear_meshing": self._small_gear_asset = Articulation(self.cfg_task.small_gear_cfg) self._large_gear_asset = Articulation(self.cfg_task.large_gear_cfg) @@ -191,7 +178,7 @@ def _setup_scene(self): self.scene.articulations["robot"] = self._robot self.scene.articulations["fixed_asset"] = self._fixed_asset self.scene.articulations["held_asset"] = self._held_asset - if self.cfg_task.name == 'gear_meshing': + if self.cfg_task.name == "gear_meshing": self.scene.articulations["small_gear"] = self._small_gear_asset self.scene.articulations["large_gear"] = self._large_gear_asset @@ -200,7 +187,7 @@ def _setup_scene(self): light_cfg.func("/World/Light", light_cfg) def _compute_intermediate_values(self, dt): - """ Get values computed from raw tensors. This includes adding noise. """ + """Get values computed from raw tensors. This includes adding noise.""" # TODO: A lot of these can probably only be set once? self.fixed_pos = self._fixed_asset.data.root_pos_w - self.scene.env_origins self.fixed_quat = self._fixed_asset.data.root_quat_w @@ -228,8 +215,8 @@ def _compute_intermediate_values(self, dt): # Add state differences if velocity isn't being added. rot_diff_quat = torch_utils.quat_mul( - self.fingertip_midpoint_quat, - torch_utils.quat_conjugate(self.prev_fingertip_quat)) + self.fingertip_midpoint_quat, torch_utils.quat_conjugate(self.prev_fingertip_quat) + ) rot_diff_quat *= torch.sign(rot_diff_quat[:, 0]).unsqueeze(-1) rot_diff_aa = quat_to_angle_axis(rot_diff_quat) @@ -242,78 +229,81 @@ def _compute_intermediate_values(self, dt): # Keypoint tensors. self.held_base_quat[:], self.held_base_pos[:] = torch_utils.tf_combine( - self.held_quat, self.held_pos, - self.held_base_quat_local, self.held_base_pos_local) + self.held_quat, self.held_pos, self.held_base_quat_local, self.held_base_pos_local + ) self.target_held_base_quat[:], self.target_held_base_pos[:] = torch_utils.tf_combine( - self.fixed_quat, self.fixed_pos, - self.identity_quat, self.fixed_success_pos_local) + self.fixed_quat, self.fixed_pos, self.identity_quat, self.fixed_success_pos_local + ) # Compute pos of keypoints on held asset, and fixed asset in world frame for idx, keypoint_offset in enumerate(self.keypoint_offsets): self.keypoints_held[:, idx] = torch_utils.tf_combine( - self.held_base_quat, self.held_base_pos, - self.identity_quat, keypoint_offset.repeat(self.num_envs, 1))[1] + self.held_base_quat, self.held_base_pos, self.identity_quat, keypoint_offset.repeat(self.num_envs, 1) + )[1] self.keypoints_fixed[:, idx] = torch_utils.tf_combine( - self.target_held_base_quat, self.target_held_base_pos, - self.identity_quat, keypoint_offset.repeat(self.num_envs, 1))[1] + self.target_held_base_quat, + self.target_held_base_pos, + self.identity_quat, + keypoint_offset.repeat(self.num_envs, 1), + )[1] - self.keypoint_dist = torch.norm( - self.keypoints_held - self.keypoints_fixed, - p=2, dim=-1).mean(-1) + self.keypoint_dist = torch.norm(self.keypoints_held - self.keypoints_fixed, p=2, dim=-1).mean(-1) self.last_update_timestamp = self._robot._data._sim_timestamp def _get_observations(self): - """ Get actor/critic inputs using assymetric critic. """ + """Get actor/critic inputs using asymmetric critic.""" noisy_fixed_pos = self.fixed_pos_obs_frame + self.init_fixed_pos_obs_noise prev_actions = self.actions.clone() obs_dict = { - 'fingertip_pos': self.fingertip_midpoint_pos, - 'fingertip_pos_rel_fixed': self.fingertip_midpoint_pos - noisy_fixed_pos, - 'fingertip_quat': self.fingertip_midpoint_quat, - 'ee_linvel': self.ee_linvel_fd, - 'ee_angvel': self.ee_angvel_fd, - 'prev_actions': prev_actions + "fingertip_pos": self.fingertip_midpoint_pos, + "fingertip_pos_rel_fixed": self.fingertip_midpoint_pos - noisy_fixed_pos, + "fingertip_quat": self.fingertip_midpoint_quat, + "ee_linvel": self.ee_linvel_fd, + "ee_angvel": self.ee_angvel_fd, + "prev_actions": prev_actions, } state_dict = { - 'fingertip_pos': self.fingertip_midpoint_pos, - 'fingertip_pos_rel_fixed': self.fingertip_midpoint_pos - self.fixed_pos_obs_frame, - 'fingertip_quat': self.fingertip_midpoint_quat, - 'ee_linvel': self.fingertip_midpoint_linvel, - 'ee_angvel': self.fingertip_midpoint_angvel, - 'joint_pos': self.joint_pos[:, 0:7], - 'held_pos': self.held_pos, - 'held_pos_rel_fixed': self.held_pos - self.fixed_pos_obs_frame, - 'held_quat': self.held_quat, - 'fixed_pos': self.fixed_pos, - 'fixed_quat': self.fixed_quat, - 'task_prop_gains': self.task_prop_gains, - 'pos_threshold': self.pos_threshold, - 'rot_threshold': self.rot_threshold, - 'prev_actions': prev_actions + "fingertip_pos": self.fingertip_midpoint_pos, + "fingertip_pos_rel_fixed": self.fingertip_midpoint_pos - self.fixed_pos_obs_frame, + "fingertip_quat": self.fingertip_midpoint_quat, + "ee_linvel": self.fingertip_midpoint_linvel, + "ee_angvel": self.fingertip_midpoint_angvel, + "joint_pos": self.joint_pos[:, 0:7], + "held_pos": self.held_pos, + "held_pos_rel_fixed": self.held_pos - self.fixed_pos_obs_frame, + "held_quat": self.held_quat, + "fixed_pos": self.fixed_pos, + "fixed_quat": self.fixed_quat, + "task_prop_gains": self.task_prop_gains, + "pos_threshold": self.pos_threshold, + "rot_threshold": self.rot_threshold, + "prev_actions": prev_actions, } - obs_tensors = [obs_dict[obs_name] for obs_name in self.cfg.obs_order + ['prev_actions']] + obs_tensors = [obs_dict[obs_name] for obs_name in self.cfg.obs_order + ["prev_actions"]] obs_tensors = torch.cat(obs_tensors, dim=-1) - state_tensors = [state_dict[state_name] for state_name in self.cfg.state_order + ['prev_actions']] + state_tensors = [state_dict[state_name] for state_name in self.cfg.state_order + ["prev_actions"]] state_tensors = torch.cat(state_tensors, dim=-1) - return {'policy': obs_tensors, 'critic': state_tensors} + return {"policy": obs_tensors, "critic": state_tensors} def _reset_buffers(self, env_ids): - """ Reset buffers. """ + """Reset buffers.""" self.ep_succeeded[env_ids] = 0 def _pre_physics_step(self, action): - """ Apply policy actions with smoothing. """ + """Apply policy actions with smoothing.""" env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1) if len(env_ids) > 0: self._reset_buffers(env_ids) - self.actions = self.cfg.ctrl.ema_factor*action.clone().to(self.device) + (1-self.cfg.ctrl.ema_factor)*self.actions + self.actions = ( + self.cfg.ctrl.ema_factor * action.clone().to(self.device) + (1 - self.cfg.ctrl.ema_factor) * self.actions + ) def close_gripper_in_place(self): - """ Keep gripper in current position as gripper closes. """ + """Keep gripper in current position as gripper closes.""" actions = torch.zeros((self.num_envs, 6), device=self.device) ctrl_target_gripper_dof_pos = 0.0 @@ -333,29 +323,23 @@ def close_gripper_in_place(self): rot_actions_quat = torch.where( angle.unsqueeze(-1).repeat(1, 4) > 1.0e-6, rot_actions_quat, - torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).repeat(self.num_envs, 1) + torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).repeat(self.num_envs, 1), ) - self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_mul( - rot_actions_quat, - self.fingertip_midpoint_quat) + self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_mul(rot_actions_quat, self.fingertip_midpoint_quat) - target_euler_xyz = torch.stack( - torch_utils.get_euler_xyz(self.ctrl_target_fingertip_midpoint_quat), - dim=1) + target_euler_xyz = torch.stack(torch_utils.get_euler_xyz(self.ctrl_target_fingertip_midpoint_quat), dim=1) target_euler_xyz[:, 0] = 3.14159 target_euler_xyz[:, 1] = 0.0 self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz( - roll=target_euler_xyz[:, 0], - pitch=target_euler_xyz[:, 1], - yaw=target_euler_xyz[:, 2] + roll=target_euler_xyz[:, 0], pitch=target_euler_xyz[:, 1], yaw=target_euler_xyz[:, 2] ) self.ctrl_target_gripper_dof_pos = ctrl_target_gripper_dof_pos self.generate_ctrl_signals() def _apply_action(self): - """ Apply actions for policy as delta targets from current position. """ + """Apply actions for policy as delta targets from current position.""" # Get current yaw for success checking. _, _, curr_yaw = torch_utils.get_euler_xyz(self.fingertip_midpoint_quat) self.curr_yaw = torch.where(curr_yaw > np.deg2rad(235), curr_yaw - 2 * np.pi, curr_yaw) @@ -377,7 +361,9 @@ def _apply_action(self): self.ctrl_target_fingertip_midpoint_pos = self.fingertip_midpoint_pos + pos_actions # To speed up learning, never allow the policy to move more than 5cm away from the base. delta_pos = self.ctrl_target_fingertip_midpoint_pos - self.fixed_pos_action_frame - pos_error_clipped = torch.clip(delta_pos, -self.cfg.ctrl.pos_action_bounds[0], self.cfg.ctrl.pos_action_bounds[1]) + pos_error_clipped = torch.clip( + delta_pos, -self.cfg.ctrl.pos_action_bounds[0], self.cfg.ctrl.pos_action_bounds[1] + ) self.ctrl_target_fingertip_midpoint_pos = self.fixed_pos_action_frame + pos_error_clipped # Convert to quat and set rot target @@ -388,37 +374,33 @@ def _apply_action(self): rot_actions_quat = torch.where( angle.unsqueeze(-1).repeat(1, 4) > 1e-6, rot_actions_quat, - torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).repeat( - self.num_envs, 1 - ) + torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).repeat(self.num_envs, 1), ) self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_mul(rot_actions_quat, self.fingertip_midpoint_quat) - + target_euler_xyz = torch.stack(torch_utils.get_euler_xyz(self.ctrl_target_fingertip_midpoint_quat), dim=1) target_euler_xyz[:, 0] = 3.14159 # Restrict actions to be upright. target_euler_xyz[:, 1] = 0.0 self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz( - roll=target_euler_xyz[:, 0], - pitch=target_euler_xyz[:, 1], - yaw=target_euler_xyz[:, 2] + roll=target_euler_xyz[:, 0], pitch=target_euler_xyz[:, 1], yaw=target_euler_xyz[:, 2] ) self.ctrl_target_gripper_dof_pos = 0.0 self.generate_ctrl_signals() - def _set_gains(self, prop_gains, rot_deriv_scale=1.): - """ Set robot gains using critical damping. """ + def _set_gains(self, prop_gains, rot_deriv_scale=1.0): + """Set robot gains using critical damping.""" self.task_prop_gains = prop_gains self.task_deriv_gains = 2 * torch.sqrt(prop_gains) self.task_deriv_gains[:, 3:6] /= rot_deriv_scale def generate_ctrl_signals(self): - """ Get Jacobian. Set Franka DOF position targets (fingers) or DOF torques (arm). """ + """Get Jacobian. Set Franka DOF position targets (fingers) or DOF torques (arm).""" self.joint_torque, self.applied_wrench = fc.compute_dof_torque( cfg=self.cfg, dof_pos=self.joint_pos, - dof_vel=self.joint_vel,#_fd, + dof_vel=self.joint_vel, # _fd, fingertip_midpoint_pos=self.fingertip_midpoint_pos, fingertip_midpoint_quat=self.fingertip_midpoint_quat, fingertip_midpoint_linvel=self.ee_linvel_fd, @@ -429,7 +411,8 @@ def generate_ctrl_signals(self): ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat, task_prop_gains=self.task_prop_gains, task_deriv_gains=self.task_deriv_gains, - device=self.device) + device=self.device, + ) # set target for gripper joints to use GYM's PD controller self.ctrl_target_joint_pos[:, 7:9] = self.ctrl_target_gripper_dof_pos @@ -439,38 +422,30 @@ def generate_ctrl_signals(self): self._robot.set_joint_effort_target(self.joint_torque) def _get_dones(self): - """ Update intermediate values used for rewards and observations. """ + """Update intermediate values used for rewards and observations.""" self._compute_intermediate_values(dt=self.physics_dt) time_out = self.episode_length_buf >= self.max_episode_length - 1 return time_out, time_out def _get_curr_successes(self, success_threshold, check_rot=False): - """ Get success mask at current timestep. """ + """Get success mask at current timestep.""" curr_successes = torch.zeros((self.num_envs,), dtype=torch.bool, device=self.device) - xy_dist = torch.linalg.vector_norm( - self.target_held_base_pos[:, 0:2] - self.held_base_pos[:, 0:2], - dim=1) + xy_dist = torch.linalg.vector_norm(self.target_held_base_pos[:, 0:2] - self.held_base_pos[:, 0:2], dim=1) z_disp = self.held_base_pos[:, 2] - self.target_held_base_pos[:, 2] - is_centered = torch.where( - xy_dist < 0.0025, - torch.ones_like(curr_successes), - torch.zeros_like(curr_successes) - ) + is_centered = torch.where(xy_dist < 0.0025, torch.ones_like(curr_successes), torch.zeros_like(curr_successes)) # Height threshold to target fixed_cfg = self.cfg_task.fixed_asset_cfg - if self.cfg_task.name == 'peg_insertion': + if self.cfg_task.name == "peg_insertion" or self.cfg_task.name == "gear_meshing": height_threshold = fixed_cfg.height * success_threshold - elif self.cfg_task.name == 'gear_meshing': - height_threshold = fixed_cfg.height * success_threshold - elif self.cfg_task.name == 'nut_threading': + elif self.cfg_task.name == "nut_threading": height_threshold = fixed_cfg.thread_pitch * success_threshold else: - raise NotImplementedError('Task not implemented') - is_close_or_below = torch.where(z_disp < height_threshold, - torch.ones_like(curr_successes), - torch.zeros_like(curr_successes)) + raise NotImplementedError("Task not implemented") + is_close_or_below = torch.where( + z_disp < height_threshold, torch.ones_like(curr_successes), torch.zeros_like(curr_successes) + ) curr_successes = torch.logical_and(is_centered, is_close_or_below) if check_rot: @@ -480,12 +455,12 @@ def _get_curr_successes(self, success_threshold, check_rot=False): return curr_successes def _get_rewards(self): - """ Update rewards and compute success statistics. """ + """Update rewards and compute success statistics.""" # Get successful and failed envs at current timestep - check_rot = self.cfg_task.name == 'nut_threading' + check_rot = self.cfg_task.name == "nut_threading" curr_successes = self._get_curr_successes( - success_threshold=self.cfg_task.success_threshold, - check_rot=check_rot) + success_threshold=self.cfg_task.success_threshold, check_rot=check_rot + ) rew_buf = self._update_rew_buf(curr_successes) @@ -509,35 +484,42 @@ def _get_rewards(self): return rew_buf def _update_rew_buf(self, curr_successes): - """ Compute reward at current timestep. """ + """Compute reward at current timestep.""" rew_dict = {} # Keypoint rewards. - squashing_fn = lambda x, a, b: 1 / (torch.exp(a*x) + b + torch.exp(-a*x)) + def squashing_fn(x, a, b): + return 1 / (torch.exp(a * x) + b + torch.exp(-a * x)) + a0, b0 = self.cfg_task.keypoint_coef_baseline - rew_dict['kp_baseline'] = squashing_fn(self.keypoint_dist, a0, b0) + rew_dict["kp_baseline"] = squashing_fn(self.keypoint_dist, a0, b0) # a1, b1 = 25, 2 a1, b1 = self.cfg_task.keypoint_coef_coarse - rew_dict['kp_coarse'] = squashing_fn(self.keypoint_dist, a1, b1) + rew_dict["kp_coarse"] = squashing_fn(self.keypoint_dist, a1, b1) a2, b2 = self.cfg_task.keypoint_coef_fine # a2, b2 = 300, 0 - rew_dict['kp_fine'] = squashing_fn(self.keypoint_dist, a2, b2) + rew_dict["kp_fine"] = squashing_fn(self.keypoint_dist, a2, b2) # Action penalties. - rew_dict['action_penalty'] = torch.norm(self.actions, p=2) - rew_dict['action_grad_penalty'] = torch.norm(self.actions - self.prev_actions, p=2, dim=-1) - rew_dict['curr_engaged'] = self._get_curr_successes( - success_threshold=self.cfg_task.engage_threshold, - check_rot=False).clone().float() - rew_dict['curr_successes'] = curr_successes.clone().float() - - rew_buf = rew_dict['kp_coarse'] + rew_dict['kp_baseline'] + rew_dict['kp_fine'] \ - - rew_dict['action_penalty'] * self.cfg_task.action_penalty_scale \ - - rew_dict['action_grad_penalty'] * self.cfg_task.action_grad_penalty_scale \ - + rew_dict['curr_engaged'] + rew_dict['curr_successes'] + rew_dict["action_penalty"] = torch.norm(self.actions, p=2) + rew_dict["action_grad_penalty"] = torch.norm(self.actions - self.prev_actions, p=2, dim=-1) + rew_dict["curr_engaged"] = ( + self._get_curr_successes(success_threshold=self.cfg_task.engage_threshold, check_rot=False).clone().float() + ) + rew_dict["curr_successes"] = curr_successes.clone().float() + + rew_buf = ( + rew_dict["kp_coarse"] + + rew_dict["kp_baseline"] + + rew_dict["kp_fine"] + - rew_dict["action_penalty"] * self.cfg_task.action_penalty_scale + - rew_dict["action_grad_penalty"] * self.cfg_task.action_grad_penalty_scale + + rew_dict["curr_engaged"] + + rew_dict["curr_successes"] + ) for rew_name, rew in rew_dict.items(): - self.extras[f'logs_rew_{rew_name}'] = rew.mean() + self.extras[f"logs_rew_{rew_name}"] = rew.mean() return rew_buf @@ -549,27 +531,27 @@ def _reset_idx(self, env_ids): self._set_assets_to_default_pose(env_ids) self._set_franka_to_default_pose( - joints=[1.5178e-03, -1.9651e-01, -1.4364e-03, -1.9761, -2.7717e-04, 1.7796, 7.8556e-01], - env_ids=env_ids) + joints=[1.5178e-03, -1.9651e-01, -1.4364e-03, -1.9761, -2.7717e-04, 1.7796, 7.8556e-01], env_ids=env_ids + ) self.step_sim_no_action() self.randomize_initial_state(env_ids) def _get_target_gear_base_offset(self): - """ Get offset of target gear from the gear base asset. """ + """Get offset of target gear from the gear base asset.""" target_gear = self.cfg_task.target_gear - if target_gear == 'gear_large': + if target_gear == "gear_large": gear_base_offset = self.cfg_task.fixed_asset_cfg.large_gear_base_offset - elif target_gear == 'gear_medium': + elif target_gear == "gear_medium": gear_base_offset = self.cfg_task.fixed_asset_cfg.medium_gear_base_offset - elif target_gear == 'gear_small': + elif target_gear == "gear_small": gear_base_offset = self.cfg_task.fixed_asset_cfg.small_gear_base_offset else: - raise ValueError(f'{target_gear} not valid in this context!') + raise ValueError(f"{target_gear} not valid in this context!") return gear_base_offset def _set_assets_to_default_pose(self, env_ids): - """ Move assets to default pose before randomization. """ + """Move assets to default pose before randomization.""" held_state = self._held_asset.data.default_root_state.clone()[env_ids] held_state[:, 0:3] += self.scene.env_origins[env_ids] held_state[:, 7:] = 0.0 @@ -583,7 +565,7 @@ def _set_assets_to_default_pose(self, env_ids): self._fixed_asset.reset() def set_pos_inverse_kinematics(self, env_ids): - """ Set robot joint position using DLS IK. """ + """Set robot joint position using DLS IK.""" ik_time = 0.0 while ik_time < 0.25: # Compute error to target. @@ -592,17 +574,19 @@ def set_pos_inverse_kinematics(self, env_ids): fingertip_midpoint_quat=self.fingertip_midpoint_quat[env_ids], ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_midpoint_pos[env_ids], ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat[env_ids], - jacobian_type='geometric', - rot_error_type='axis_angle') + jacobian_type="geometric", + rot_error_type="axis_angle", + ) delta_hand_pose = torch.cat((pos_error, axis_angle_error), dim=-1) # Solve DLS problem. delta_dof_pos = fc._get_delta_dof_pos( delta_pose=delta_hand_pose, - ik_method='dls', + ik_method="dls", jacobian=self.fingertip_midpoint_jacobian[env_ids], - device=self.device) + device=self.device, + ) self.joint_pos[env_ids, 0:7] += delta_dof_pos[:, 0:7] self.joint_vel[env_ids, :] = torch.zeros_like(self.joint_pos[env_ids,]) @@ -619,38 +603,37 @@ def set_pos_inverse_kinematics(self, env_ids): return pos_error, axis_angle_error def get_handheld_asset_relative_pose(self): - """ Get default relative pose between help asset and fingertip. """ - if self.cfg_task.name == 'peg_insertion': + """Get default relative pose between help asset and fingertip.""" + if self.cfg_task.name == "peg_insertion": held_asset_relative_pos = torch.zeros_like(self.held_base_pos_local) held_asset_relative_pos[:, 2] = self.cfg_task.held_asset_cfg.height held_asset_relative_pos[:, 2] -= self.cfg_task.robot_cfg.franka_fingerpad_length - elif self.cfg_task.name == 'gear_meshing': + elif self.cfg_task.name == "gear_meshing": held_asset_relative_pos = torch.zeros_like(self.held_base_pos_local) gear_base_offset = self._get_target_gear_base_offset() held_asset_relative_pos[:, 0] += gear_base_offset[0] held_asset_relative_pos[:, 2] += gear_base_offset[2] held_asset_relative_pos[:, 2] += self.cfg_task.held_asset_cfg.height / 2.0 * 1.1 - elif self.cfg_task.name == 'nut_threading': + elif self.cfg_task.name == "nut_threading": held_asset_relative_pos = self.held_base_pos_local else: - raise NotImplementedError('Task not implemented') + raise NotImplementedError("Task not implemented") held_asset_relative_quat = self.identity_quat - if self.cfg_task.name == 'nut_threading': + if self.cfg_task.name == "nut_threading": # Rotate along z-axis of frame for default position. initial_rot_deg = self.cfg_task.held_asset_rot_init - rot_yaw_euler = torch.tensor( - [0., 0., initial_rot_deg * np.pi / 180.0], device=self.device - ).repeat(self.num_envs, 1) + rot_yaw_euler = torch.tensor([0.0, 0.0, initial_rot_deg * np.pi / 180.0], device=self.device).repeat( + self.num_envs, 1 + ) held_asset_relative_quat = torch_utils.quat_from_euler_xyz( - roll=rot_yaw_euler[:, 0], - pitch=rot_yaw_euler[:, 1], - yaw=rot_yaw_euler[:, 2]) + roll=rot_yaw_euler[:, 0], pitch=rot_yaw_euler[:, 1], yaw=rot_yaw_euler[:, 2] + ) return held_asset_relative_pos, held_asset_relative_quat def _set_franka_to_default_pose(self, joints, env_ids): - """ Return Franka to its default joint position. """ + """Return Franka to its default joint position.""" gripper_width = self.cfg_task.held_asset_cfg.diameter / 2 * 1.25 joint_pos = self._robot.data.default_joint_pos[env_ids] joint_pos[:, 7:] = gripper_width # MIMIC @@ -658,7 +641,7 @@ def _set_franka_to_default_pose(self, joints, env_ids): joint_vel = torch.zeros_like(joint_pos) joint_effort = torch.zeros_like(joint_pos) self.ctrl_target_joint_pos[env_ids, :] = joint_pos - print(f'Resetting {len(env_ids)} envs...') + print(f"Resetting {len(env_ids)} envs...") self._robot.set_joint_position_target(self.ctrl_target_joint_pos[env_ids], env_ids=env_ids) self._robot.write_joint_state_to_sim(joint_pos, joint_vel, env_ids=env_ids) self._robot.reset() @@ -667,14 +650,14 @@ def _set_franka_to_default_pose(self, joints, env_ids): self.step_sim_no_action() def step_sim_no_action(self): - """ Step the simulation without an action. Used for resets. """ + """Step the simulation without an action. Used for resets.""" self.scene.write_data_to_sim() self.sim.step(render=True) self.scene.update(dt=self.physics_dt) self._compute_intermediate_values(dt=self.physics_dt) def randomize_initial_state(self, env_ids): - """ Randomize initial state and perform any episode-level randomization. """ + """Randomize initial state and perform any episode-level randomization.""" # Disable gravity. physics_sim_view = sim_utils.SimulationContext.instance().physics_sim_view physics_sim_view.set_gravity(carb.Float3(0.0, 0.0, 0.0)) @@ -685,8 +668,8 @@ def randomize_initial_state(self, env_ids): rand_sample = torch.rand((len(env_ids), 3), dtype=torch.float32, device=self.device) fixed_pos_init_rand = 2 * (rand_sample - 0.5) # [-1, 1] fixed_asset_init_pos_rand = torch.tensor( - self.cfg_task.fixed_asset_init_pos_noise, - dtype=torch.float32, device=self.device) + self.cfg_task.fixed_asset_init_pos_noise, dtype=torch.float32, device=self.device + ) fixed_pos_init_rand = fixed_pos_init_rand @ torch.diag(fixed_asset_init_pos_rand) fixed_state[:, 0:3] += fixed_pos_init_rand + self.scene.env_origins[env_ids] # (1.b.) Orientation @@ -694,12 +677,11 @@ def randomize_initial_state(self, env_ids): fixed_orn_yaw_range = np.deg2rad(self.cfg_task.fixed_asset_init_orn_range_deg) rand_sample = torch.rand((len(env_ids), 3), dtype=torch.float32, device=self.device) fixed_orn_euler = fixed_orn_init_yaw + fixed_orn_yaw_range * rand_sample - fixed_orn_euler[:, 0:2] = 0. # Only change yaw. + fixed_orn_euler[:, 0:2] = 0.0 # Only change yaw. fixed_orn_quat = torch_utils.quat_from_euler_xyz( - fixed_orn_euler[:, 0], - fixed_orn_euler[:, 1], - fixed_orn_euler[:, 2]) - fixed_state[:, 3:7] = fixed_orn_quat + fixed_orn_euler[:, 0], fixed_orn_euler[:, 1], fixed_orn_euler[:, 2] + ) + fixed_state[:, 3:7] = fixed_orn_quat # (1.c.) Velocity fixed_state[:, 7:] = 0.0 # vel # (1.d.) Update values. @@ -708,9 +690,7 @@ def randomize_initial_state(self, env_ids): # (1.e.) Noisy position observation. fixed_asset_pos_noise = torch.randn((len(env_ids), 3), dtype=torch.float32, device=self.device) - fixed_asset_pos_rand = torch.tensor( - self.cfg.obs_rand.fixed_asset_pos, - dtype=torch.float32, device=self.device) + fixed_asset_pos_rand = torch.tensor(self.cfg.obs_rand.fixed_asset_pos, dtype=torch.float32, device=self.device) fixed_asset_pos_noise = fixed_asset_pos_noise @ torch.diag(fixed_asset_pos_rand) self.init_fixed_pos_obs_noise[:] = fixed_asset_pos_noise @@ -721,12 +701,11 @@ def randomize_initial_state(self, env_ids): fixed_tip_pos_local = torch.zeros_like(self.fixed_pos) fixed_tip_pos_local[:, 2] += self.cfg_task.fixed_asset_cfg.height fixed_tip_pos_local[:, 2] += self.cfg_task.fixed_asset_cfg.base_height - if self.cfg_task.name == 'gear_meshing': + if self.cfg_task.name == "gear_meshing": fixed_tip_pos_local[:, 0] = self._get_target_gear_base_offset()[0] _, fixed_tip_pos = torch_utils.tf_combine( - self.fixed_quat, self.fixed_pos, - self.identity_quat, fixed_tip_pos_local + self.fixed_quat, self.fixed_pos, self.identity_quat, fixed_tip_pos_local ) self.fixed_pos_obs_frame[:] = fixed_tip_pos @@ -750,9 +729,9 @@ def randomize_initial_state(self, env_ids): above_fixed_pos[bad_envs] += above_fixed_pos_rand # (b) get random orientation facing down - hand_down_euler = torch.tensor( - self.cfg_task.hand_init_orn, device=self.device - ).unsqueeze(0).repeat(n_bad, 1) + hand_down_euler = ( + torch.tensor(self.cfg_task.hand_init_orn, device=self.device).unsqueeze(0).repeat(n_bad, 1) + ) rand_sample = torch.rand((n_bad, 3), dtype=torch.float32, device=self.device) above_fixed_orn_noise = 2 * (rand_sample - 0.5) # [-1, 1] @@ -761,9 +740,8 @@ def randomize_initial_state(self, env_ids): hand_down_euler += above_fixed_orn_noise self.hand_down_euler[bad_envs, ...] = hand_down_euler hand_down_quat[bad_envs, :] = torch_utils.quat_from_euler_xyz( - roll=hand_down_euler[:, 0], - pitch=hand_down_euler[:, 1], - yaw=hand_down_euler[:, 2]) + roll=hand_down_euler[:, 0], pitch=hand_down_euler[:, 1], yaw=hand_down_euler[:, 2] + ) # (c) iterative IK Method self.ctrl_target_fingertip_midpoint_pos[bad_envs, ...] = above_fixed_pos[bad_envs, ...] @@ -776,20 +754,20 @@ def randomize_initial_state(self, env_ids): bad_envs = bad_envs[any_error.nonzero(as_tuple=False).squeeze(-1)] if bad_envs.shape[0] == 0: - print('Done IK') + print("Done IK") break self._set_franka_to_default_pose( - joints=[0.00871, -0.10368, -0.00794, -1.49139, -0.00083, 1.38774, 0.0], - env_ids=bad_envs) + joints=[0.00871, -0.10368, -0.00794, -1.49139, -0.00083, 1.38774, 0.0], env_ids=bad_envs + ) ik_attempt += 1 - print(f'IK Attempt: {ik_attempt}\tBad Envs: {bad_envs.shape[0]}') + print(f"IK Attempt: {ik_attempt}\tBad Envs: {bad_envs.shape[0]}") self.step_sim_no_action() # Add flanking gears after servo (so arm doesn't move them). - if self.cfg_task.name == 'gear_meshing' and self.cfg_task.add_flanking_gears: + if self.cfg_task.name == "gear_meshing" and self.cfg_task.add_flanking_gears: small_gear_state = self._small_gear_asset.data.default_root_state.clone()[env_ids] small_gear_state[:, 0:7] = fixed_state[:, 0:7] small_gear_state[:, 7:] = 0.0 # vel @@ -804,31 +782,28 @@ def randomize_initial_state(self, env_ids): # (3) Randomize asset-in-gripper location. # flip gripper z orientation - flip_z_quat = torch.tensor( - [0.0, 0.0, 1.0, 0.0], device=self.device - ).unsqueeze(0).repeat(self.num_envs, 1) + flip_z_quat = torch.tensor([0.0, 0.0, 1.0, 0.0], device=self.device).unsqueeze(0).repeat(self.num_envs, 1) fingertip_flipped_quat, fingertip_flipped_pos = torch_utils.tf_combine( q1=self.fingertip_midpoint_quat, t1=self.fingertip_midpoint_pos, q2=flip_z_quat, - t2=torch.zeros_like(self.fingertip_midpoint_pos)) + t2=torch.zeros_like(self.fingertip_midpoint_pos), + ) # get default gripper in asset transform held_asset_relative_pos, held_asset_relative_quat = self.get_handheld_asset_relative_pose() asset_in_hand_quat, asset_in_hand_pos = torch_utils.tf_inverse( - held_asset_relative_quat, - held_asset_relative_pos) + held_asset_relative_quat, held_asset_relative_pos + ) translated_held_asset_quat, translated_held_asset_pos = torch_utils.tf_combine( - q1=fingertip_flipped_quat, - t1=fingertip_flipped_pos, - q2=asset_in_hand_quat, - t2=asset_in_hand_pos) + q1=fingertip_flipped_quat, t1=fingertip_flipped_pos, q2=asset_in_hand_quat, t2=asset_in_hand_pos + ) # Add asset in hand randomization rand_sample = torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device) self.held_asset_pos_noise = 2 * (rand_sample - 0.5) # [-1, 1] - if self.cfg_task.name == 'gear_meshing': + if self.cfg_task.name == "gear_meshing": self.held_asset_pos_noise[:, 2] = -rand_sample[:, 2] # [-1, 0] held_asset_pos_noise = torch.tensor(self.cfg_task.held_asset_pos_noise, device=self.device) @@ -837,7 +812,8 @@ def randomize_initial_state(self, env_ids): q1=translated_held_asset_quat, t1=translated_held_asset_pos, q2=self.identity_quat, - t2=self.held_asset_pos_noise) + t2=self.held_asset_pos_noise, + ) held_state = self._held_asset.data.default_root_state.clone() held_state[:, 0:3] = translated_held_asset_pos + self.scene.env_origins @@ -848,10 +824,9 @@ def randomize_initial_state(self, env_ids): # Close hand # Set gains to use for quick resets. - reset_task_prop_gains = torch.tensor( - self.cfg.ctrl.reset_task_prop_gains, - device=self.device - ).repeat((self.num_envs, 1)) + reset_task_prop_gains = torch.tensor(self.cfg.ctrl.reset_task_prop_gains, device=self.device).repeat( + (self.num_envs, 1) + ) reset_rot_deriv_scale = self.cfg.ctrl.reset_rot_deriv_scale self._set_gains(reset_task_prop_gains, reset_rot_deriv_scale) @@ -868,7 +843,7 @@ def randomize_initial_state(self, env_ids): diff = self.target_held_base_pos - self.held_base_pos bad_idxs = env_ids[diff[:, 1] > 0.05] - print('Bad:', bad_idxs) + print("Bad:", bad_idxs) self.prev_joint_pos = self.joint_pos[:, 0:7].clone() self.prev_fingertip_pos = self.fingertip_midpoint_pos.clone() @@ -883,32 +858,25 @@ def randomize_initial_state(self, env_ids): pos_actions = self.fingertip_midpoint_pos - self.fixed_pos_action_frame pos_action_bounds = torch.tensor(self.cfg.ctrl.pos_action_bounds, device=self.device) - pos_actions = pos_actions @ torch.diag(1./pos_action_bounds) + pos_actions = pos_actions @ torch.diag(1.0 / pos_action_bounds) self.actions[:, 0:3] = self.prev_actions[:, 0:3] = pos_actions # Relative yaw to bolt. - unrot_180_euler = torch.tensor( - [-np.pi, 0., 0.], device=self.device - ).repeat(self.num_envs, 1) + unrot_180_euler = torch.tensor([-np.pi, 0.0, 0.0], device=self.device).repeat(self.num_envs, 1) unrot_quat = torch_utils.quat_from_euler_xyz( - roll=unrot_180_euler[:, 0], - pitch=unrot_180_euler[:, 1], - yaw=unrot_180_euler[:, 2]) + roll=unrot_180_euler[:, 0], pitch=unrot_180_euler[:, 1], yaw=unrot_180_euler[:, 2] + ) fingertip_quat_rel_bolt = torch_utils.quat_mul(unrot_quat, self.fingertip_midpoint_quat) fingertip_yaw_bolt = torch_utils.get_euler_xyz(fingertip_quat_rel_bolt)[-1] fingertip_yaw_bolt = torch.where( - fingertip_yaw_bolt > torch.pi / 2, - fingertip_yaw_bolt - 2 * torch.pi, - fingertip_yaw_bolt + fingertip_yaw_bolt > torch.pi / 2, fingertip_yaw_bolt - 2 * torch.pi, fingertip_yaw_bolt ) fingertip_yaw_bolt = torch.where( - fingertip_yaw_bolt < -torch.pi, - fingertip_yaw_bolt + 2 * torch.pi, - fingertip_yaw_bolt + fingertip_yaw_bolt < -torch.pi, fingertip_yaw_bolt + 2 * torch.pi, fingertip_yaw_bolt ) - yaw_action = (fingertip_yaw_bolt + np.deg2rad(180.)) / np.deg2rad(270.) * 2. - 1. + yaw_action = (fingertip_yaw_bolt + np.deg2rad(180.0)) / np.deg2rad(270.0) * 2.0 - 1.0 self.actions[:, 5] = self.prev_actions[:, 5] = yaw_action # Zero initial velocity. @@ -919,4 +887,4 @@ def randomize_initial_state(self, env_ids): self._set_gains(self.default_gains) physics_sim_view.set_gravity(carb.Float3(*self.cfg.sim.gravity)) - print('Done Reset') + print("Done Reset") diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py index db2ac3b643..a541e33b59 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py @@ -1,45 +1,51 @@ -import omni.isaac.lab.sim as sim_utils +# Copyright (c) 2022-2024, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +import omni.isaac.lab.sim as sim_utils from omni.isaac.lab.actuators.actuator_cfg import ImplicitActuatorCfg from omni.isaac.lab.assets import ArticulationCfg from omni.isaac.lab.envs import DirectRLEnvCfg from omni.isaac.lab.scene import InteractiveSceneCfg from omni.isaac.lab.sim import PhysxCfg, SimulationCfg -from omni.isaac.lab.utils import configclass from omni.isaac.lab.sim.spawners.materials.physics_materials_cfg import RigidBodyMaterialCfg +from omni.isaac.lab.utils import configclass -from .factory_tasks_cfg import PegInsertion, GearMeshing, NutThreading, ASSET_DIR +from .factory_tasks_cfg import ASSET_DIR, GearMeshing, NutThreading, PegInsertion OBS_DIM_CFG = { - 'fingertip_pos': 3, - 'fingertip_pos_rel_fixed': 3, - 'fingertip_quat': 4, - 'ee_linvel': 3, - 'ee_angvel': 3, + "fingertip_pos": 3, + "fingertip_pos_rel_fixed": 3, + "fingertip_quat": 4, + "ee_linvel": 3, + "ee_angvel": 3, } STATE_DIM_CFG = { - 'fingertip_pos': 3, - 'fingertip_pos_rel_fixed': 3, - 'fingertip_quat': 4, - 'ee_linvel': 3, - 'ee_angvel': 3, - 'joint_pos': 7, - 'held_pos': 3, - 'held_pos_rel_fixed': 3, - 'held_quat': 4, - 'fixed_pos': 3, - 'fixed_quat': 4, - 'task_prop_gains': 6, - 'ema_factor': 1, - 'pos_threshold': 3, - 'rot_threshold': 3 + "fingertip_pos": 3, + "fingertip_pos_rel_fixed": 3, + "fingertip_quat": 4, + "ee_linvel": 3, + "ee_angvel": 3, + "joint_pos": 7, + "held_pos": 3, + "held_pos_rel_fixed": 3, + "held_quat": 4, + "fixed_pos": 3, + "fixed_quat": 4, + "task_prop_gains": 6, + "ema_factor": 1, + "pos_threshold": 3, + "rot_threshold": 3, } + @configclass class ObsRandCfg: fixed_asset_pos = [0.001, 0.001, 0.001] + @configclass class CtrlCfg: ema_factor = 0.2 @@ -54,10 +60,11 @@ class CtrlCfg: reset_rot_deriv_scale = 10.0 default_task_prop_gains = [100, 100, 100, 30, 30, 30] - default_dof_pos_tensor = [-1.3003, -0.4015, 1.1791, -2.1493, 0.4001, 1.9425, 0.4754] + default_dof_pos_tensor = [-1.3003, -0.4015, 1.1791, -2.1493, 0.4001, 1.9425, 0.4754] kp_null = 10.0 kd_null = 6.3246 + @configclass class FactoryEnvCfg(DirectRLEnvCfg): decimation = 8 @@ -65,29 +72,22 @@ class FactoryEnvCfg(DirectRLEnvCfg): # num_*: will be overwritten to correspond to obs_order, state_order. observation_space = 21 state_space = 72 - obs_order: list = [ - 'fingertip_pos_rel_fixed', - 'fingertip_quat', - 'ee_linvel', - 'ee_angvel'] + obs_order: list = ["fingertip_pos_rel_fixed", "fingertip_quat", "ee_linvel", "ee_angvel"] state_order: list = [ - 'fingertip_pos', - 'fingertip_quat', - 'ee_linvel', - 'ee_angvel', - 'joint_pos', - 'held_pos', - 'held_pos_rel_fixed', - 'held_quat', - 'fixed_pos', - 'fixed_quat'] - - task_name: str = 'peg_insertion' # peg_insertion, gear_meshing, nut_threading - tasks: dict = { - 'peg_insertion': PegInsertion(), - 'gear_meshing': GearMeshing(), - 'nut_threading': NutThreading() - } + "fingertip_pos", + "fingertip_quat", + "ee_linvel", + "ee_angvel", + "joint_pos", + "held_pos", + "held_pos_rel_fixed", + "held_quat", + "fixed_pos", + "fixed_quat", + ] + + task_name: str = "peg_insertion" # peg_insertion, gear_meshing, nut_threading + tasks: dict = {"peg_insertion": PegInsertion(), "gear_meshing": GearMeshing(), "nut_threading": NutThreading()} # task: FactoryTask = PegInsertion() # PegInsertion, GearMeshing, NutThreading obs_rand: ObsRandCfg = ObsRandCfg() ctrl: CtrlCfg = CtrlCfg() @@ -95,7 +95,7 @@ class FactoryEnvCfg(DirectRLEnvCfg): episode_length_s = 10.0 # Probably need to override. sim: SimulationCfg = SimulationCfg( device="cuda:0", - dt=1/120, + dt=1 / 120, gravity=(0.0, 0.0, -9.81), physx=PhysxCfg( solver_type=1, @@ -106,7 +106,7 @@ class FactoryEnvCfg(DirectRLEnvCfg): friction_correlation_distance=0.00625, gpu_max_rigid_contact_count=2**23, gpu_max_rigid_patch_count=2**23, - gpu_max_num_partitions=1 # Important for stable simulation. + gpu_max_num_partitions=1, # Important for stable simulation. ), physics_material=RigidBodyMaterialCfg( static_friction=1.0, @@ -114,15 +114,12 @@ class FactoryEnvCfg(DirectRLEnvCfg): ), ) - scene: InteractiveSceneCfg = InteractiveSceneCfg( - num_envs=128, - env_spacing=2.0 - ) + scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=128, env_spacing=2.0) robot = ArticulationCfg( prim_path="/World/envs/env_.*/Robot", spawn=sim_utils.UsdFileCfg( - usd_path=f'{ASSET_DIR}/franka_mimic.usd', + usd_path=f"{ASSET_DIR}/franka_mimic.usd", activate_contact_sensors=True, rigid_props=sim_utils.RigidBodyPropertiesCfg( disable_gravity=True, @@ -141,18 +138,15 @@ class FactoryEnvCfg(DirectRLEnvCfg): solver_position_iteration_count=192, solver_velocity_iteration_count=1, ), - collision_props=sim_utils.CollisionPropertiesCfg( - contact_offset=0.005, - rest_offset=0.0 - ), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), ), init_state=ArticulationCfg.InitialStateCfg( joint_pos={ - "panda_joint1": 0.00871, - "panda_joint2": -0.10368, - "panda_joint3": -0.00794, - "panda_joint4": -1.49139, - "panda_joint5": -0.00083, + "panda_joint1": 0.00871, + "panda_joint2": -0.10368, + "panda_joint3": -0.00794, + "panda_joint4": -1.49139, + "panda_joint5": -0.00083, "panda_joint6": 1.38774, "panda_joint7": 0.0, "panda_finger_joint2": 0.04, @@ -168,7 +162,7 @@ class FactoryEnvCfg(DirectRLEnvCfg): friction=0.0, armature=0.0, effort_limit=87, - velocity_limit=124.6 + velocity_limit=124.6, ), "panda_arm2": ImplicitActuatorCfg( joint_names_expr=["panda_joint[5-7]"], @@ -177,7 +171,7 @@ class FactoryEnvCfg(DirectRLEnvCfg): friction=0.0, armature=0.0, effort_limit=12, - velocity_limit=149.5 + velocity_limit=149.5, ), "panda_hand": ImplicitActuatorCfg( joint_names_expr=["panda_finger_joint[1-2]"], diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py index 2c411ce585..4038830d1e 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py @@ -1,37 +1,45 @@ -from omni.isaac.lab.utils import configclass -from omni.isaac.lab.assets import ArticulationCfg +# Copyright (c) 2022-2024, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause import omni.isaac.lab.sim as sim_utils +from omni.isaac.lab.assets import ArticulationCfg +from omni.isaac.lab.utils import configclass + +ASSET_DIR = "" # Should be replaced with location to Factory assets -ASSET_DIR = '../IsaacLab_benchmark/source/extensions/omni.isaac.lab_assets/data/Factory' @configclass class FixedAssetCfg: - usd_path: str = '' + usd_path: str = "" diameter: float = 0.0 height: float = 0.0 - base_height: float = 0.0 # Used to compute held asset CoM. + base_height: float = 0.0 # Used to compute held asset CoM. friction: float = 0.75 mass: float = 0.05 + @configclass class HeldAssetCfg: - usd_path: str = '' - diameter: float = 0.0 # Used for gripper width. + usd_path: str = "" + diameter: float = 0.0 # Used for gripper width. height: float = 0.0 friction: float = 0.75 mass: float = 0.05 + @configclass class RobotCfg: - robot_usd: str = '' + robot_usd: str = "" franka_fingerpad_length: float = 0.017608 friction: float = 0.75 + @configclass class FactoryTask: robot_cfg: RobotCfg = RobotCfg() - name: str = '' + name: str = "" duration_s = 5.0 fixed_asset_cfg: FixedAssetCfg = FixedAssetCfg() @@ -42,7 +50,7 @@ class FactoryTask: hand_init_pos: list = [0.0, 0.0, 0.015] # Relative to fixed asset tip. hand_init_pos_noise: list = [0.02, 0.02, 0.01] hand_init_orn: list = [3.1416, 0, 2.356] - hand_init_orn_noise: list = [0., 0., 1.57] + hand_init_orn_noise: list = [0.0, 0.0, 1.57] # Action unidirectional_rot: bool = False @@ -77,21 +85,23 @@ class FactoryTask: @configclass class Peg8mm(HeldAssetCfg): - usd_path = f'{ASSET_DIR}/factory_peg_8mm.usd' + usd_path = f"{ASSET_DIR}/factory_peg_8mm.usd" diameter = 0.007986 height = 0.050 mass = 0.019 + @configclass class Hole8mm(FixedAssetCfg): - usd_path = f'{ASSET_DIR}/factory_hole_8mm.usd' + usd_path = f"{ASSET_DIR}/factory_hole_8mm.usd" diameter = 0.0081 height = 0.025 base_height = 0.0 + @configclass class PegInsertion(FactoryTask): - name = 'peg_insertion' + name = "peg_insertion" fixed_asset_cfg = Hole8mm() held_asset_cfg = Peg8mm() asset_size = 8.0 @@ -138,18 +148,12 @@ class PegInsertion(FactoryTask): max_contact_impulse=1e32, ), mass_props=sim_utils.MassPropertiesCfg(mass=fixed_asset_cfg.mass), - collision_props=sim_utils.CollisionPropertiesCfg( - contact_offset=0.005, - rest_offset=0.0 - ), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), ), init_state=ArticulationCfg.InitialStateCfg( - pos=(0.6, 0.0, 0.05), - rot=(1.0, 0.0, 0.0, 0.0), - joint_pos={}, - joint_vel={} + pos=(0.6, 0.0, 0.05), rot=(1.0, 0.0, 0.0, 0.0), joint_pos={}, joint_vel={} ), - actuators={} + actuators={}, ) held_asset: ArticulationCfg = ArticulationCfg( prim_path="/World/envs/env_.*/HeldAsset", @@ -169,47 +173,43 @@ class PegInsertion(FactoryTask): max_contact_impulse=1e32, ), mass_props=sim_utils.MassPropertiesCfg(mass=held_asset_cfg.mass), - collision_props=sim_utils.CollisionPropertiesCfg( - contact_offset=0.005, - rest_offset=0.0 - ), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), ), init_state=ArticulationCfg.InitialStateCfg( - pos=(0.0, 0.4, 0.1), - rot=(1.0, 0.0, 0.0, 0.0), - joint_pos={}, - joint_vel={} + pos=(0.0, 0.4, 0.1), rot=(1.0, 0.0, 0.0, 0.0), joint_pos={}, joint_vel={} ), - actuators={} + actuators={}, ) + @configclass class GearBase(FixedAssetCfg): - usd_path = f'{ASSET_DIR}/factory_gear_base.usd' + usd_path = f"{ASSET_DIR}/factory_gear_base.usd" height = 0.02 base_height = 0.005 small_gear_base_offset = [5.075e-2, 0.0, 0.0] medium_gear_base_offset = [2.025e-2, 0.0, 0.0] large_gear_base_offset = [-3.025e-2, 0.0, 0.0] + @configclass class MediumGear(HeldAssetCfg): - usd_path = f'{ASSET_DIR}/factory_gear_medium.usd' - diameter = 0.03 # Used for gripper width. + usd_path = f"{ASSET_DIR}/factory_gear_medium.usd" + diameter = 0.03 # Used for gripper width. height: float = 0.03 mass = 0.012 @configclass class GearMeshing(FactoryTask): - name = 'gear_meshing' + name = "gear_meshing" fixed_asset_cfg = GearBase() held_asset_cfg = MediumGear() - target_gear = 'gear_medium' + target_gear = "gear_medium" duration_s = 20.0 - small_gear_usd = f'{ASSET_DIR}/factory_gear_small.usd' - large_gear_usd = f'{ASSET_DIR}/factory_gear_large.usd' + small_gear_usd = f"{ASSET_DIR}/factory_gear_small.usd" + large_gear_usd = f"{ASSET_DIR}/factory_gear_large.usd" small_gear_cfg: ArticulationCfg = ArticulationCfg( prim_path="/World/envs/env_.*/SmallGearAsset", @@ -229,18 +229,12 @@ class GearMeshing(FactoryTask): max_contact_impulse=1e32, ), mass_props=sim_utils.MassPropertiesCfg(mass=0.019), - collision_props=sim_utils.CollisionPropertiesCfg( - contact_offset=0.005, - rest_offset=0.0 - ), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), ), init_state=ArticulationCfg.InitialStateCfg( - pos=(0.0, 0.4, 0.1), - rot=(1.0, 0.0, 0.0, 0.0), - joint_pos={}, - joint_vel={} + pos=(0.0, 0.4, 0.1), rot=(1.0, 0.0, 0.0, 0.0), joint_pos={}, joint_vel={} ), - actuators={} + actuators={}, ) large_gear_cfg: ArticulationCfg = ArticulationCfg( @@ -261,18 +255,12 @@ class GearMeshing(FactoryTask): max_contact_impulse=1e32, ), mass_props=sim_utils.MassPropertiesCfg(mass=0.019), - collision_props=sim_utils.CollisionPropertiesCfg( - contact_offset=0.005, - rest_offset=0.0 - ), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), ), init_state=ArticulationCfg.InitialStateCfg( - pos=(0.0, 0.4, 0.1), - rot=(1.0, 0.0, 0.0, 0.0), - joint_pos={}, - joint_vel={} + pos=(0.0, 0.4, 0.1), rot=(1.0, 0.0, 0.0, 0.0), joint_pos={}, joint_vel={} ), - actuators={} + actuators={}, ) # Gears Asset @@ -283,7 +271,7 @@ class GearMeshing(FactoryTask): hand_init_pos: list = [0.0, 0.0, 0.035] # Relative to fixed asset tip. hand_init_pos_noise: list = [0.02, 0.02, 0.01] hand_init_orn: list = [3.1416, 0, 0.0] - hand_init_orn_noise: list = [0., 0., 0.785] + hand_init_orn_noise: list = [0.0, 0.0, 0.785] # Fixed Asset (applies to all tasks) fixed_asset_init_pos_noise: list = [0.05, 0.05, 0.05] @@ -319,18 +307,12 @@ class GearMeshing(FactoryTask): max_contact_impulse=1e32, ), mass_props=sim_utils.MassPropertiesCfg(mass=fixed_asset_cfg.mass), - collision_props=sim_utils.CollisionPropertiesCfg( - contact_offset=0.005, - rest_offset=0.0 - ), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), ), init_state=ArticulationCfg.InitialStateCfg( - pos=(0.6, 0.0, 0.05), - rot=(1.0, 0.0, 0.0, 0.0), - joint_pos={}, - joint_vel={} + pos=(0.6, 0.0, 0.05), rot=(1.0, 0.0, 0.0, 0.0), joint_pos={}, joint_vel={} ), - actuators={} + actuators={}, ) held_asset: ArticulationCfg = ArticulationCfg( prim_path="/World/envs/env_.*/HeldAsset", @@ -350,39 +332,36 @@ class GearMeshing(FactoryTask): max_contact_impulse=1e32, ), mass_props=sim_utils.MassPropertiesCfg(mass=held_asset_cfg.mass), - collision_props=sim_utils.CollisionPropertiesCfg( - contact_offset=0.005, - rest_offset=0.0 - ), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), ), init_state=ArticulationCfg.InitialStateCfg( - pos=(0.0, 0.4, 0.1), - rot=(1.0, 0.0, 0.0, 0.0), - joint_pos={}, - joint_vel={} + pos=(0.0, 0.4, 0.1), rot=(1.0, 0.0, 0.0, 0.0), joint_pos={}, joint_vel={} ), - actuators={} + actuators={}, ) + @configclass class NutM16(HeldAssetCfg): - usd_path = f'{ASSET_DIR}/factory_nut_m16.usd' + usd_path = f"{ASSET_DIR}/factory_nut_m16.usd" diameter = 0.024 height = 0.01 mass = 0.03 friction = 0.01 # Additive with the nut means friction is (-0.25 + 0.75)/2 = 0.25 + @configclass class BoltM16(FixedAssetCfg): - usd_path = f'{ASSET_DIR}/factory_bolt_m16.usd' + usd_path = f"{ASSET_DIR}/factory_bolt_m16.usd" diameter = 0.024 height = 0.025 base_height = 0.01 thread_pitch = 0.002 + @configclass class NutThreading(FactoryTask): - name = 'nut_threading' + name = "nut_threading" fixed_asset_cfg = BoltM16() held_asset_cfg = NutM16() asset_size = 16.0 @@ -434,18 +413,12 @@ class NutThreading(FactoryTask): max_contact_impulse=1e32, ), mass_props=sim_utils.MassPropertiesCfg(mass=fixed_asset_cfg.mass), - collision_props=sim_utils.CollisionPropertiesCfg( - contact_offset=0.005, - rest_offset=0.0 - ), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), ), init_state=ArticulationCfg.InitialStateCfg( - pos=(0.6, 0.0, 0.05), - rot=(1.0, 0.0, 0.0, 0.0), - joint_pos={}, - joint_vel={} + pos=(0.6, 0.0, 0.05), rot=(1.0, 0.0, 0.0, 0.0), joint_pos={}, joint_vel={} ), - actuators={} + actuators={}, ) held_asset: ArticulationCfg = ArticulationCfg( prim_path="/World/envs/env_.*/HeldAsset", @@ -465,16 +438,10 @@ class NutThreading(FactoryTask): max_contact_impulse=1e32, ), mass_props=sim_utils.MassPropertiesCfg(mass=held_asset_cfg.mass), - collision_props=sim_utils.CollisionPropertiesCfg( - contact_offset=0.005, - rest_offset=0.0 - ), + collision_props=sim_utils.CollisionPropertiesCfg(contact_offset=0.005, rest_offset=0.0), ), init_state=ArticulationCfg.InitialStateCfg( - pos=(0.0, 0.4, 0.1), - rot=(1.0, 0.0, 0.0, 0.0), - joint_pos={}, - joint_vel={} + pos=(0.0, 0.4, 0.1), rot=(1.0, 0.0, 0.0, 0.0), joint_pos={}, joint_vel={} ), - actuators={} + actuators={}, ) diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/torch_jit_utils.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/torch_jit_utils.py index 484641ec61..ed57fb6e12 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/torch_jit_utils.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/torch_jit_utils.py @@ -1,46 +1,24 @@ -# Copyright (c) 2018-2022, NVIDIA Corporation +# Copyright (c) 2022-2024, The Isaac Lab Project Developers. # All rights reserved. # -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# 1. Redistributions of source code must retain the above copyright notice, this -# list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# -# 3. Neither the name of the copyright holder nor the names of its -# contributors may be used to endorse or promote products derived from -# this software without specific prior written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +# SPDX-License-Identifier: BSD-3-Clause import torch -import omni.isaac.core.utils.torch as torch_utils +import omni.isaac.core.utils.torch as torch_utils """ Rotation conversions """ + @torch.jit.script def quat_to_angle_axis(q): # type: (Tensor) -> Tuple[Tensor, Tensor] # computes axis-angle representation from quaternion q # q must be normalized min_theta = 1e-5 - qx, qy, qz, qw = 1, 2, 3, 0 + qx, qw = 1, 0 sin_theta = torch.sqrt(1 - q[..., qw] * q[..., qw]) angle = 2 * torch.acos(q[..., qw]) From f5009315f09bd890fd250f417d16cbc65eca6a19 Mon Sep 17 00:00:00 2001 From: Michael Noseworthy Date: Mon, 9 Dec 2024 19:48:10 -0500 Subject: [PATCH 06/15] Update docs and changelog --- docs/source/overview/environments.rst | 6 +++--- .../omni.isaac.lab_tasks/config/extension.toml | 2 +- .../extensions/omni.isaac.lab_tasks/docs/CHANGELOG.rst | 10 ++++++++++ 3 files changed, 14 insertions(+), 4 deletions(-) diff --git a/docs/source/overview/environments.rst b/docs/source/overview/environments.rst index cd993824d7..f0adaf3241 100644 --- a/docs/source/overview/environments.rst +++ b/docs/source/overview/environments.rst @@ -181,9 +181,9 @@ For example: .. |factory-gear| image:: ../_static/tasks/factory/gear_meshing.png .. |factory-nut| image:: ../_static/tasks/factory/nut_bolt_fastening.png -.. |factory-peg-link| replace:: `Factory-Direct-v0 env.task_name=peg_insertion `__ -.. |factory-gear-link| replace:: `Factory-Direct-v0 env.task_name=gear_meshing `__ -.. |factory-nut-link| replace:: `Factory-Direct-v0 env.task_name=nut_threading `__ +.. |factory-peg-link| replace:: `Factory-Direct-v0 env.task_name=peg_insertion env.episode_length_s=10.0 `__ +.. |factory-gear-link| replace:: `Factory-Direct-v0 env.task_name=gear_meshing env.episode_length_s=20.0 `__ +.. |factory-nut-link| replace:: `Factory-Direct-v0 env.task_name=nut_threading env.episode_length_s=30.0 `__ Locomotion ~~~~~~~~~~ diff --git a/source/extensions/omni.isaac.lab_tasks/config/extension.toml b/source/extensions/omni.isaac.lab_tasks/config/extension.toml index ba976faf92..accf06c5c5 100644 --- a/source/extensions/omni.isaac.lab_tasks/config/extension.toml +++ b/source/extensions/omni.isaac.lab_tasks/config/extension.toml @@ -1,7 +1,7 @@ [package] # Note: Semantic Versioning is used: https://semver.org/ -version = "0.10.14" +version = "0.10.15" # Description title = "Isaac Lab Environments" diff --git a/source/extensions/omni.isaac.lab_tasks/docs/CHANGELOG.rst b/source/extensions/omni.isaac.lab_tasks/docs/CHANGELOG.rst index 068db9b64a..d049c611a5 100644 --- a/source/extensions/omni.isaac.lab_tasks/docs/CHANGELOG.rst +++ b/source/extensions/omni.isaac.lab_tasks/docs/CHANGELOG.rst @@ -1,6 +1,16 @@ Changelog --------- +0.10.15 (2024-12-09) +~~~~~~~~~~~~~~~~~~~~ + +Added +^^^^^ + +* Added ``Factory-Direct-v0`` environment as a direct RL env that + implements contact-rich manipulation tasks including peg insertion, + gear meshing, and nut threading. + 0.10.14 (2024-12-03) ~~~~~~~~~~~~~~~~~~~~ From 79f554415057274fb51920eb96394b98a7ac4bf9 Mon Sep 17 00:00:00 2001 From: Michael Noseworthy Date: Mon, 9 Dec 2024 23:48:35 -0500 Subject: [PATCH 07/15] Add contributors for Factory envs --- CONTRIBUTORS.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index 9b3ea57d63..5a597896eb 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -49,6 +49,7 @@ Guidelines for modifications: * Giulio Romualdi * Haoran Zhou * HoJin Jeon +* Iretiayo Akinola * Jan Kerner * Jean Tampon * Jia Lin Yuan @@ -62,6 +63,7 @@ Guidelines for modifications: * Lorenz Wellhausen * Masoud Moghani * Michael Gussert +* Michael Noseworthy * Muhong Guo * Nuralem Abizov * Oyindamola Omotuyi @@ -90,3 +92,4 @@ Guidelines for modifications: * Gavriel State * Hammad Mazhar * Marco Hutter +* Yashraj Narang From 2aa8aa535c18e8dbf5afc3066fe75857abfc2803 Mon Sep 17 00:00:00 2001 From: Michael Noseworthy Date: Wed, 11 Dec 2024 08:18:37 -0500 Subject: [PATCH 08/15] Fix indentation of player config --- .../lab_tasks/direct/factory/agents/rl_games_ppo_cfg.yaml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/agents/rl_games_ppo_cfg.yaml b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/agents/rl_games_ppo_cfg.yaml index 9c31db0854..5494199846 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/agents/rl_games_ppo_cfg.yaml +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/agents/rl_games_ppo_cfg.yaml @@ -113,5 +113,6 @@ params: before_mlp: True concat_input: True layer_norm: True - player: - deterministic: False + + player: + deterministic: False From e81909a97214b7acbf2a21f95eac3b9c515ee0b4 Mon Sep 17 00:00:00 2001 From: Michael Noseworthy Date: Wed, 11 Dec 2024 08:50:46 -0500 Subject: [PATCH 09/15] Register each contact-rich task using a separate configclass --- docs/source/overview/environments.rst | 6 +-- .../lab_tasks/direct/factory/__init__.py | 27 +++++++++++-- .../lab_tasks/direct/factory/factory_env.py | 38 +++++++++---------- .../direct/factory/factory_env_cfg.py | 28 ++++++++++++-- .../direct/factory/factory_tasks_cfg.py | 14 +++---- 5 files changed, 77 insertions(+), 36 deletions(-) diff --git a/docs/source/overview/environments.rst b/docs/source/overview/environments.rst index f0adaf3241..808e1fd609 100644 --- a/docs/source/overview/environments.rst +++ b/docs/source/overview/environments.rst @@ -181,9 +181,9 @@ For example: .. |factory-gear| image:: ../_static/tasks/factory/gear_meshing.png .. |factory-nut| image:: ../_static/tasks/factory/nut_bolt_fastening.png -.. |factory-peg-link| replace:: `Factory-Direct-v0 env.task_name=peg_insertion env.episode_length_s=10.0 `__ -.. |factory-gear-link| replace:: `Factory-Direct-v0 env.task_name=gear_meshing env.episode_length_s=20.0 `__ -.. |factory-nut-link| replace:: `Factory-Direct-v0 env.task_name=nut_threading env.episode_length_s=30.0 `__ +.. |factory-peg-link| replace:: `Factory-PegInsert-Direct-v0 `__ +.. |factory-gear-link| replace:: `Factory-GearMesh-Direct-v0 `__ +.. |factory-nut-link| replace:: `Factory-NutThread-Direct-v0 `__ Locomotion ~~~~~~~~~~ diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/__init__.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/__init__.py index 3c0e92dd39..cf6b418a76 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/__init__.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/__init__.py @@ -6,18 +6,39 @@ import gymnasium as gym from . import agents -from .factory_env import FactoryEnv, FactoryEnvCfg +from .factory_env import FactoryEnv +from .factory_env_cfg import FactoryTaskGearMeshCfg, FactoryTaskNutThreadCfg, FactoryTaskPegInsertCfg ## # Register Gym environments. ## gym.register( - id="Factory-Direct-v0", + id="Factory-PegInsert-Direct-v0", entry_point="omni.isaac.lab_tasks.direct.factory:FactoryEnv", disable_env_checker=True, kwargs={ - "env_cfg_entry_point": FactoryEnvCfg, + "env_cfg_entry_point": FactoryTaskPegInsertCfg, + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + }, +) + +gym.register( + id="Factory-GearMesh-Direct-v0", + entry_point="omni.isaac.lab_tasks.direct.factory:FactoryEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": FactoryTaskGearMeshCfg, + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + }, +) + +gym.register( + id="Factory-NutThread-Direct-v0", + entry_point="omni.isaac.lab_tasks.direct.factory:FactoryEnv", + disable_env_checker=True, + kwargs={ + "env_cfg_entry_point": FactoryTaskNutThreadCfg, "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", }, ) diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py index 69c01bb2aa..532cc895b0 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py @@ -29,7 +29,7 @@ def __init__(self, cfg: FactoryEnvCfg, render_mode: str | None = None, **kwargs) cfg.state_space = sum([STATE_DIM_CFG[state] for state in cfg.state_order]) cfg.observation_space += cfg.action_space cfg.state_space += cfg.action_space - self.cfg_task = cfg.tasks[cfg.task_name] + self.cfg_task = cfg.task super().__init__(cfg, render_mode, **kwargs) @@ -90,13 +90,13 @@ def _init_tensors(self): # Held asset held_base_x_offset = 0.0 - if self.cfg_task.name == "peg_insertion": + if self.cfg_task.name == "peg_insert": held_base_z_offset = 0.0 - elif self.cfg_task.name == "gear_meshing": + elif self.cfg_task.name == "gear_mesh": gear_base_offset = self._get_target_gear_base_offset() held_base_x_offset = gear_base_offset[0] held_base_z_offset = gear_base_offset[2] - elif self.cfg_task.name == "nut_threading": + elif self.cfg_task.name == "nut_thread": held_base_z_offset = self.cfg_task.fixed_asset_cfg.base_height else: raise NotImplementedError("Task not implemented") @@ -131,13 +131,13 @@ def _init_tensors(self): # Used to compute target poses. self.fixed_success_pos_local = torch.zeros((self.num_envs, 3), device=self.device) - if self.cfg_task.name == "peg_insertion": + if self.cfg_task.name == "peg_insert": self.fixed_success_pos_local[:, 2] = 0.0 - elif self.cfg_task.name == "gear_meshing": + elif self.cfg_task.name == "gear_mesh": gear_base_offset = self._get_target_gear_base_offset() self.fixed_success_pos_local[:, 0] = gear_base_offset[0] self.fixed_success_pos_local[:, 2] = gear_base_offset[2] - elif self.cfg_task.name == "nut_threading": + elif self.cfg_task.name == "nut_thread": head_height = self.cfg_task.fixed_asset_cfg.base_height shank_length = self.cfg_task.fixed_asset_cfg.height thread_pitch = self.cfg_task.fixed_asset_cfg.thread_pitch @@ -168,7 +168,7 @@ def _setup_scene(self): self._robot = Articulation(self.cfg.robot) self._fixed_asset = Articulation(self.cfg_task.fixed_asset) self._held_asset = Articulation(self.cfg_task.held_asset) - if self.cfg_task.name == "gear_meshing": + if self.cfg_task.name == "gear_mesh": self._small_gear_asset = Articulation(self.cfg_task.small_gear_cfg) self._large_gear_asset = Articulation(self.cfg_task.large_gear_cfg) @@ -178,7 +178,7 @@ def _setup_scene(self): self.scene.articulations["robot"] = self._robot self.scene.articulations["fixed_asset"] = self._fixed_asset self.scene.articulations["held_asset"] = self._held_asset - if self.cfg_task.name == "gear_meshing": + if self.cfg_task.name == "gear_mesh": self.scene.articulations["small_gear"] = self._small_gear_asset self.scene.articulations["large_gear"] = self._large_gear_asset @@ -437,9 +437,9 @@ def _get_curr_successes(self, success_threshold, check_rot=False): is_centered = torch.where(xy_dist < 0.0025, torch.ones_like(curr_successes), torch.zeros_like(curr_successes)) # Height threshold to target fixed_cfg = self.cfg_task.fixed_asset_cfg - if self.cfg_task.name == "peg_insertion" or self.cfg_task.name == "gear_meshing": + if self.cfg_task.name == "peg_insert" or self.cfg_task.name == "gear_mesh": height_threshold = fixed_cfg.height * success_threshold - elif self.cfg_task.name == "nut_threading": + elif self.cfg_task.name == "nut_thread": height_threshold = fixed_cfg.thread_pitch * success_threshold else: raise NotImplementedError("Task not implemented") @@ -457,7 +457,7 @@ def _get_curr_successes(self, success_threshold, check_rot=False): def _get_rewards(self): """Update rewards and compute success statistics.""" # Get successful and failed envs at current timestep - check_rot = self.cfg_task.name == "nut_threading" + check_rot = self.cfg_task.name == "nut_thread" curr_successes = self._get_curr_successes( success_threshold=self.cfg_task.success_threshold, check_rot=check_rot ) @@ -604,23 +604,23 @@ def set_pos_inverse_kinematics(self, env_ids): def get_handheld_asset_relative_pose(self): """Get default relative pose between help asset and fingertip.""" - if self.cfg_task.name == "peg_insertion": + if self.cfg_task.name == "peg_insert": held_asset_relative_pos = torch.zeros_like(self.held_base_pos_local) held_asset_relative_pos[:, 2] = self.cfg_task.held_asset_cfg.height held_asset_relative_pos[:, 2] -= self.cfg_task.robot_cfg.franka_fingerpad_length - elif self.cfg_task.name == "gear_meshing": + elif self.cfg_task.name == "gear_mesh": held_asset_relative_pos = torch.zeros_like(self.held_base_pos_local) gear_base_offset = self._get_target_gear_base_offset() held_asset_relative_pos[:, 0] += gear_base_offset[0] held_asset_relative_pos[:, 2] += gear_base_offset[2] held_asset_relative_pos[:, 2] += self.cfg_task.held_asset_cfg.height / 2.0 * 1.1 - elif self.cfg_task.name == "nut_threading": + elif self.cfg_task.name == "nut_thread": held_asset_relative_pos = self.held_base_pos_local else: raise NotImplementedError("Task not implemented") held_asset_relative_quat = self.identity_quat - if self.cfg_task.name == "nut_threading": + if self.cfg_task.name == "nut_thread": # Rotate along z-axis of frame for default position. initial_rot_deg = self.cfg_task.held_asset_rot_init rot_yaw_euler = torch.tensor([0.0, 0.0, initial_rot_deg * np.pi / 180.0], device=self.device).repeat( @@ -701,7 +701,7 @@ def randomize_initial_state(self, env_ids): fixed_tip_pos_local = torch.zeros_like(self.fixed_pos) fixed_tip_pos_local[:, 2] += self.cfg_task.fixed_asset_cfg.height fixed_tip_pos_local[:, 2] += self.cfg_task.fixed_asset_cfg.base_height - if self.cfg_task.name == "gear_meshing": + if self.cfg_task.name == "gear_mesh": fixed_tip_pos_local[:, 0] = self._get_target_gear_base_offset()[0] _, fixed_tip_pos = torch_utils.tf_combine( @@ -767,7 +767,7 @@ def randomize_initial_state(self, env_ids): self.step_sim_no_action() # Add flanking gears after servo (so arm doesn't move them). - if self.cfg_task.name == "gear_meshing" and self.cfg_task.add_flanking_gears: + if self.cfg_task.name == "gear_mesh" and self.cfg_task.add_flanking_gears: small_gear_state = self._small_gear_asset.data.default_root_state.clone()[env_ids] small_gear_state[:, 0:7] = fixed_state[:, 0:7] small_gear_state[:, 7:] = 0.0 # vel @@ -803,7 +803,7 @@ def randomize_initial_state(self, env_ids): # Add asset in hand randomization rand_sample = torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device) self.held_asset_pos_noise = 2 * (rand_sample - 0.5) # [-1, 1] - if self.cfg_task.name == "gear_meshing": + if self.cfg_task.name == "gear_mesh": self.held_asset_pos_noise[:, 2] = -rand_sample[:, 2] # [-1, 0] held_asset_pos_noise = torch.tensor(self.cfg_task.held_asset_pos_noise, device=self.device) diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py index a541e33b59..dd20dec216 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py @@ -12,7 +12,7 @@ from omni.isaac.lab.sim.spawners.materials.physics_materials_cfg import RigidBodyMaterialCfg from omni.isaac.lab.utils import configclass -from .factory_tasks_cfg import ASSET_DIR, GearMeshing, NutThreading, PegInsertion +from .factory_tasks_cfg import ASSET_DIR, FactoryTask, GearMesh, NutThread, PegInsert OBS_DIM_CFG = { "fingertip_pos": 3, @@ -86,9 +86,8 @@ class FactoryEnvCfg(DirectRLEnvCfg): "fixed_quat", ] - task_name: str = "peg_insertion" # peg_insertion, gear_meshing, nut_threading - tasks: dict = {"peg_insertion": PegInsertion(), "gear_meshing": GearMeshing(), "nut_threading": NutThreading()} - # task: FactoryTask = PegInsertion() # PegInsertion, GearMeshing, NutThreading + task_name: str = "peg_insert" # peg_insert, gear_mesh, nut_thread + task: FactoryTask = FactoryTask() obs_rand: ObsRandCfg = ObsRandCfg() ctrl: CtrlCfg = CtrlCfg() @@ -184,3 +183,24 @@ class FactoryEnvCfg(DirectRLEnvCfg): ), }, ) + + +@configclass +class FactoryTaskPegInsertCfg(FactoryEnvCfg): + task_name = "peg_insert" + task = PegInsert() + episode_length_s = 10.0 + + +@configclass +class FactoryTaskGearMeshCfg(FactoryEnvCfg): + task_name = "gear_mesh" + task = GearMesh() + episode_length_s = 20.0 + + +@configclass +class FactoryTaskNutThreadCfg(FactoryEnvCfg): + task_name = "nut_thread" + task = NutThread() + episode_length_s = 30.0 diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py index 4038830d1e..07f723f87d 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py @@ -65,7 +65,7 @@ class FactoryTask: held_asset_rot_init: float = -90.0 # Reward - ee_success_yaw: float = 0.0 # nut_threading task only. + ee_success_yaw: float = 0.0 # nut_thread task only. action_penalty_scale: float = 0.0 action_grad_penalty_scale: float = 0.0 # Reward function details can be found in Appendix B of https://arxiv.org/pdf/2408.04587. @@ -100,8 +100,8 @@ class Hole8mm(FixedAssetCfg): @configclass -class PegInsertion(FactoryTask): - name = "peg_insertion" +class PegInsert(FactoryTask): + name = "peg_insert" fixed_asset_cfg = Hole8mm() held_asset_cfg = Peg8mm() asset_size = 8.0 @@ -201,8 +201,8 @@ class MediumGear(HeldAssetCfg): @configclass -class GearMeshing(FactoryTask): - name = "gear_meshing" +class GearMesh(FactoryTask): + name = "gear_mesh" fixed_asset_cfg = GearBase() held_asset_cfg = MediumGear() target_gear = "gear_medium" @@ -360,8 +360,8 @@ class BoltM16(FixedAssetCfg): @configclass -class NutThreading(FactoryTask): - name = "nut_threading" +class NutThread(FactoryTask): + name = "nut_thread" fixed_asset_cfg = BoltM16() held_asset_cfg = NutM16() asset_size = 16.0 From 3fdd2de1735d3eae1ac6f4bc4a8955e2d9d81d50 Mon Sep 17 00:00:00 2001 From: Michael Noseworthy Date: Thu, 12 Dec 2024 10:59:25 -0500 Subject: [PATCH 10/15] Update ASSET_DIR for Factory assets --- .../omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py index 07f723f87d..643cbc2afb 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_tasks_cfg.py @@ -6,8 +6,9 @@ import omni.isaac.lab.sim as sim_utils from omni.isaac.lab.assets import ArticulationCfg from omni.isaac.lab.utils import configclass +from omni.isaac.lab.utils.assets import ISAACLAB_NUCLEUS_DIR -ASSET_DIR = "" # Should be replaced with location to Factory assets +ASSET_DIR = f"{ISAACLAB_NUCLEUS_DIR}/Factory" @configclass From 66c912af8dd9ef806d0ca6629e92715e85370217 Mon Sep 17 00:00:00 2001 From: Michael Noseworthy Date: Mon, 16 Dec 2024 13:08:22 -0500 Subject: [PATCH 11/15] Update from code review - Isaac- prefix added to task names - Documentation updated with jpg images - Use math.py rotation utilities - Misc code cleanup --- .../_static/tasks/factory/gear_mesh.jpg | Bin 0 -> 47279 bytes .../_static/tasks/factory/gear_meshing.png | Bin 586548 -> 0 bytes .../tasks/factory/nut_bolt_fastening.png | Bin 435793 -> 0 bytes .../_static/tasks/factory/nut_thread.jpg | Bin 0 -> 44543 bytes .../_static/tasks/factory/peg_insert.jpg | Bin 0 -> 43938 bytes .../_static/tasks/factory/peg_insertion.png | Bin 444028 -> 0 bytes docs/source/overview/environments.rst | 12 +- .../omni.isaac.lab_tasks/docs/CHANGELOG.rst | 1 + .../lab_tasks/direct/factory/__init__.py | 6 +- .../direct/factory/factory_control.py | 201 +----------------- .../lab_tasks/direct/factory/factory_env.py | 24 +-- .../direct/factory/factory_env_cfg.py | 2 + .../direct/factory/torch_jit_utils.py | 39 ---- 13 files changed, 20 insertions(+), 265 deletions(-) create mode 100644 docs/source/_static/tasks/factory/gear_mesh.jpg delete mode 100644 docs/source/_static/tasks/factory/gear_meshing.png delete mode 100644 docs/source/_static/tasks/factory/nut_bolt_fastening.png create mode 100644 docs/source/_static/tasks/factory/nut_thread.jpg create mode 100644 docs/source/_static/tasks/factory/peg_insert.jpg delete mode 100644 docs/source/_static/tasks/factory/peg_insertion.png delete mode 100644 source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/torch_jit_utils.py diff --git a/docs/source/_static/tasks/factory/gear_mesh.jpg b/docs/source/_static/tasks/factory/gear_mesh.jpg new file mode 100644 index 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zaK%+1fCx#lvFk+YDF;)Wn|h5lzB2zoF09xLpq~@1T6@ayh!;U9%$MkIC zF3?5i-CgeT%V-nix0-0C9G|Ws6qkf`|0zAUzTVXW?;l@bp58K&sov%_!Yz!`hfKQj znmiZ3RH2h<@>EP=bho6%6f6n_2LX&Y!ny&%Q=BBkgwz~Y*^!cE)alAN6j7Ra@`%9f>_Cg!$1H$ zE=SYYTg7}9{7k5(uY3Tccz>m iMsQ+JTpjfXi8uUQegwHY1Tx`__ -.. |factory-gear-link| replace:: `Factory-GearMesh-Direct-v0 `__ -.. |factory-nut-link| replace:: `Factory-NutThread-Direct-v0 `__ +.. |factory-peg-link| replace:: `Isaac-Factory-PegInsert-Direct-v0 `__ +.. |factory-gear-link| replace:: `Isaac-Factory-GearMesh-Direct-v0 `__ +.. |factory-nut-link| replace:: `Isaac-Factory-NutThread-Direct-v0 `__ Locomotion ~~~~~~~~~~ diff --git a/source/extensions/omni.isaac.lab_tasks/docs/CHANGELOG.rst b/source/extensions/omni.isaac.lab_tasks/docs/CHANGELOG.rst index d049c611a5..b8f2a76639 100644 --- a/source/extensions/omni.isaac.lab_tasks/docs/CHANGELOG.rst +++ b/source/extensions/omni.isaac.lab_tasks/docs/CHANGELOG.rst @@ -20,6 +20,7 @@ Added * Added ``Isaac-Stack-Cube-Franka-IK-Rel-v0`` and ``Isaac-Stack-Cube-Instance-Randomize-Franka-IK-Rel-v0`` environments as manager-based RL envs that implement a three cube stacking task. + 0.10.13 (2024-10-30) ~~~~~~~~~~~~~~~~~~~~ diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/__init__.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/__init__.py index cf6b418a76..c19c96f708 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/__init__.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/__init__.py @@ -14,7 +14,7 @@ ## gym.register( - id="Factory-PegInsert-Direct-v0", + id="Isaac-Factory-PegInsert-Direct-v0", entry_point="omni.isaac.lab_tasks.direct.factory:FactoryEnv", disable_env_checker=True, kwargs={ @@ -24,7 +24,7 @@ ) gym.register( - id="Factory-GearMesh-Direct-v0", + id="Isaac-Factory-GearMesh-Direct-v0", entry_point="omni.isaac.lab_tasks.direct.factory:FactoryEnv", disable_env_checker=True, kwargs={ @@ -34,7 +34,7 @@ ) gym.register( - id="Factory-NutThread-Direct-v0", + id="Isaac-Factory-NutThread-Direct-v0", entry_point="omni.isaac.lab_tasks.direct.factory:FactoryEnv", disable_env_checker=True, kwargs={ diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_control.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_control.py index 3b6165927c..33efc41ca0 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_control.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_control.py @@ -13,40 +13,7 @@ import omni.isaac.core.utils.torch as torch_utils - -def compute_dof_pos_target( - cfg_ctrl, - arm_dof_pos, - fingertip_midpoint_pos, - fingertip_midpoint_quat, - jacobian, - ctrl_target_fingertip_midpoint_pos, - ctrl_target_fingertip_midpoint_quat, - ctrl_target_gripper_dof_pos, - device, -): - """Compute Franka DOF position target to move fingertips towards target pose.""" - - ctrl_target_dof_pos = torch.zeros((cfg_ctrl["num_envs"], 9), device=device) - - pos_error, axis_angle_error = get_pose_error( - fingertip_midpoint_pos=fingertip_midpoint_pos, - fingertip_midpoint_quat=fingertip_midpoint_quat, - ctrl_target_fingertip_midpoint_pos=ctrl_target_fingertip_midpoint_pos, - ctrl_target_fingertip_midpoint_quat=ctrl_target_fingertip_midpoint_quat, - jacobian_type=cfg_ctrl["jacobian_type"], - rot_error_type="axis_angle", - ) - - delta_fingertip_pose = torch.cat((pos_error, axis_angle_error), dim=1) - delta_arm_dof_pos = _get_delta_dof_pos( - delta_pose=delta_fingertip_pose, ik_method=cfg_ctrl["ik_method"], jacobian=jacobian, device=device - ) - - ctrl_target_dof_pos[:, 0:7] = arm_dof_pos + delta_arm_dof_pos - ctrl_target_dof_pos[:, 7:9] = ctrl_target_gripper_dof_pos # gripper finger joints - - return ctrl_target_dof_pos +from omni.isaac.lab.utils.math import axis_angle_from_quat def compute_dof_torque( @@ -162,32 +129,12 @@ def get_pose_error( # Convert to axis-angle error axis_angle_error = axis_angle_from_quat(quat_error) - elif jacobian_type == "analytic": # See example 2.9.7; note use of J_a and difference of rotation vectors - # Compute axis-angle error - axis_angle_error = axis_angle_from_quat(ctrl_target_fingertip_midpoint_quat) - axis_angle_from_quat( - fingertip_midpoint_quat - ) - if rot_error_type == "quat": return pos_error, quat_error elif rot_error_type == "axis_angle": return pos_error, axis_angle_error -def _get_wrench_error(left_finger_force, right_finger_force, ctrl_target_fingertip_contact_wrench, num_envs, device): - """Compute task-space error between target Franka fingertip contact wrench and current wrench.""" - - fingertip_contact_wrench = torch.zeros((num_envs, 6), device=device) - - fingertip_contact_wrench[:, 0:3] = left_finger_force + right_finger_force # net contact force on fingers - # Cols 3 to 6 are all zeros, as we do not have enough information - - force_error = ctrl_target_fingertip_contact_wrench[:, 0:3] - (-fingertip_contact_wrench[:, 0:3]) - torque_error = ctrl_target_fingertip_contact_wrench[:, 3:6] - (-fingertip_contact_wrench[:, 3:6]) - - return force_error, torque_error - - def _get_delta_dof_pos(delta_pose, ik_method, jacobian, device): """Get delta Franka DOF position from delta pose using specified IK method.""" # References: @@ -247,149 +194,3 @@ def _apply_task_space_gains( 0.0 - fingertip_midpoint_angvel ) return task_wrench - - -def get_analytic_jacobian(fingertip_quat, fingertip_jacobian, num_envs, device): - """Convert geometric Jacobian to analytic Jacobian.""" - # Reference: https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf - # NOTE: Gym returns world-space geometric Jacobians by default - - batch = num_envs - - # Overview: - # x = [x_p; x_r] - # From eq. 2.189 and 2.192, x_dot = J_a @ q_dot = (E_inv @ J_g) @ q_dot - # From eq. 2.191, E = block(E_p, E_r); thus, E_inv = block(E_p_inv, E_r_inv) - # Eq. 2.12 gives an expression for E_p_inv - # Eq. 2.107 gives an expression for E_r_inv - - # Compute E_inv_top (i.e., [E_p_inv, 0]) - eye = torch.eye(3, device=device) - E_p_inv = eye.repeat((batch, 1)).reshape(batch, 3, 3) - E_inv_top = torch.cat((E_p_inv, torch.zeros((batch, 3, 3), device=device)), dim=2) - - # Compute E_inv_bottom (i.e., [0, E_r_inv]) - fingertip_axis_angle = axis_angle_from_quat(fingertip_quat) - fingertip_axis_angle_cross = get_skew_symm_matrix(fingertip_axis_angle, device=device) - fingertip_angle = torch.linalg.vector_norm(fingertip_axis_angle, dim=1) - factor_1 = 1 / (fingertip_angle**2) - factor_2 = 1 - fingertip_angle * 0.5 * torch.sin(fingertip_angle) / (1 - torch.cos(fingertip_angle)) - factor_3 = factor_1 * factor_2 - E_r_inv = ( - eye - - 1 * 0.5 * fingertip_axis_angle_cross - + (fingertip_axis_angle_cross @ fingertip_axis_angle_cross) - * factor_3.unsqueeze(-1).repeat((1, 3 * 3)).reshape((batch, 3, 3)) - ) - E_inv_bottom = torch.cat((torch.zeros((batch, 3, 3), device=device), E_r_inv), dim=2) - - E_inv = torch.cat((E_inv_top.reshape((batch, 3 * 6)), E_inv_bottom.reshape((batch, 3 * 6))), dim=1).reshape( - (batch, 6, 6) - ) - - J_a = E_inv @ fingertip_jacobian - - return J_a - - -def get_skew_symm_matrix(vec, device): - """Convert vector to skew-symmetric matrix.""" - # Reference: https://en.wikipedia.org/wiki/Cross_product#Conversion_to_matrix_multiplication - - batch = vec.shape[0] - eye = torch.eye(3, device=device) - skew_symm = torch.transpose( - torch.cross(vec.repeat((1, 3)).reshape((batch * 3, 3)), eye.repeat((batch, 1))).reshape(batch, 3, 3), - dim0=1, - dim1=2, - ) - - return skew_symm - - -def translate_along_local_z(pos, quat, offset, device): - """Translate global body position along local Z-axis and express in global coordinates.""" - - num_vecs = pos.shape[0] - offset_vec = offset * torch.tensor([0.0, 0.0, 1.0], device=device).repeat((num_vecs, 1)) - _, translated_pos = torch_utils.tf_combine( - q1=quat, t1=pos, q2=torch.tensor([1.0, 0.0, 0.0, 0.0], device=device).repeat((num_vecs, 1)), t2=offset_vec - ) - - return translated_pos - - -def translate_along_local_xyz(pos, quat, offset_vec, device): - """Translate global body position along local frame and express in global coordinates.""" - - num_vecs = pos.shape[0] - _, translated_pos = torch_utils.tf_combine( - q1=quat, t1=pos, q2=torch.tensor([1.0, 0.0, 0.0, 0.0], device=device).repeat((num_vecs, 1)), t2=offset_vec - ) - - return translated_pos - - -def axis_angle_from_euler(euler): - """Convert tensor of Euler angles to tensor of axis-angles.""" - - quat = torch_utils.quat_from_euler_xyz(roll=euler[:, 0], pitch=euler[:, 1], yaw=euler[:, 2]) - quat = quat * torch.sign(quat[:, 0]).unsqueeze(-1) # smaller rotation - axis_angle = axis_angle_from_quat(quat) - - return axis_angle - - -def axis_angle_from_quat(quat, eps=1.0e-6): - """Convert tensor of quaternions to tensor of axis-angles.""" - # Reference: https://github.com/facebookresearch/pytorch3d/blob/bee31c48d3d36a8ea268f9835663c52ff4a476ec/pytorch3d/transforms/rotation_conversions.py#L516-L544 - - mag = torch.linalg.norm(quat[:, 1:], dim=1) - half_angle = torch.atan2(mag, quat[:, 0]) - angle = 2.0 * half_angle - sin_half_angle_over_angle = torch.where( - torch.abs(angle) > eps, torch.sin(half_angle) / angle, 1.0 / 2.0 - angle**2.0 / 48 - ) - - axis_angle = quat[:, 1:] / sin_half_angle_over_angle.unsqueeze(-1) - - return axis_angle - - -def axis_angle_from_quat_naive(quat): - """Convert tensor of quaternions to tensor of axis-angles.""" - # Reference: https://en.wikipedia.org/wiki/quats_and_spatial_rotation#Recovering_the_axis-angle_representation - # NOTE: Susceptible to undesirable behavior due to divide-by-zero - - mag = torch.linalg.vector_norm(quat[:, 1:], dim=1) # zero when quat = [0, 0, 0, 1] - axis = quat[:, 1:] / mag.unsqueeze(-1) - angle = 2.0 * torch.atan2(mag, quat[:, 0]) - axis_angle = axis * angle.unsqueeze(-1) - - return axis_angle - - -def get_rand_quat(num_quats, device): - """Generate tensor of random quaternions.""" - # Reference: http://planning.cs.uiuc.edu/node198.html - - u = torch.rand((num_quats, 3), device=device) - quat = torch.zeros((num_quats, 4), device=device) - quat[:, 1] = torch.sqrt(1 - u[:, 0]) * torch.sin(2 * math.pi * u[:, 1]) - quat[:, 2] = torch.sqrt(1 - u[:, 0]) * torch.cos(2 * math.pi * u[:, 1]) - quat[:, 3] = torch.sqrt(u[:, 0]) * torch.sin(2 * math.pi * u[:, 2]) - quat[:, 0] = torch.sqrt(u[:, 0]) * torch.cos(2 * math.pi * u[:, 2]) - - return quat - - -def get_nonrand_quat(num_quats, rot_perturbation, device): - """Generate tensor of non-random quaternions by composing random Euler rotations.""" - - quat = torch_utils.quat_from_euler_xyz( - torch.rand((num_quats, 1), device=device).squeeze() * rot_perturbation * 2.0 - rot_perturbation, - torch.rand((num_quats, 1), device=device).squeeze() * rot_perturbation * 2.0 - rot_perturbation, - torch.rand((num_quats, 1), device=device).squeeze() * rot_perturbation * 2.0 - rot_perturbation, - ) - - return quat diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py index 532cc895b0..df7850d32c 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env.py @@ -14,10 +14,10 @@ from omni.isaac.lab.envs import DirectRLEnv from omni.isaac.lab.sim.spawners.from_files import GroundPlaneCfg, spawn_ground_plane from omni.isaac.lab.utils.assets import ISAAC_NUCLEUS_DIR +from omni.isaac.lab.utils.math import axis_angle_from_quat from . import factory_control as fc from .factory_env_cfg import OBS_DIM_CFG, STATE_DIM_CFG, FactoryEnvCfg -from .torch_jit_utils import quat_to_angle_axis class FactoryEnv(DirectRLEnv): @@ -218,9 +218,8 @@ def _compute_intermediate_values(self, dt): self.fingertip_midpoint_quat, torch_utils.quat_conjugate(self.prev_fingertip_quat) ) rot_diff_quat *= torch.sign(rot_diff_quat[:, 0]).unsqueeze(-1) - rot_diff_aa = quat_to_angle_axis(rot_diff_quat) - - self.ee_angvel_fd = (rot_diff_aa[0].unsqueeze(-1) * rot_diff_aa[1]) / dt + rot_diff_aa = axis_angle_from_quat(rot_diff_quat) + self.ee_angvel_fd = rot_diff_aa / dt self.prev_fingertip_quat = self.fingertip_midpoint_quat.clone() joint_diff = self.joint_pos[:, 0:7] - self.prev_joint_pos @@ -414,7 +413,7 @@ def generate_ctrl_signals(self): device=self.device, ) - # set target for gripper joints to use GYM's PD controller + # set target for gripper joints to use physx's PD controller self.ctrl_target_joint_pos[:, 7:9] = self.ctrl_target_gripper_dof_pos self.joint_torque[:, 7:9] = 0.0 @@ -530,9 +529,7 @@ def _reset_idx(self, env_ids): super()._reset_idx(env_ids) self._set_assets_to_default_pose(env_ids) - self._set_franka_to_default_pose( - joints=[1.5178e-03, -1.9651e-01, -1.4364e-03, -1.9761, -2.7717e-04, 1.7796, 7.8556e-01], env_ids=env_ids - ) + self._set_franka_to_default_pose(joints=self.cfg.ctrl.reset_joints, env_ids=env_ids) self.step_sim_no_action() self.randomize_initial_state(env_ids) @@ -593,7 +590,6 @@ def set_pos_inverse_kinematics(self, env_ids): self.ctrl_target_joint_pos[env_ids, 0:7] = self.joint_pos[env_ids, 0:7] # Update dof state. self._robot.write_joint_state_to_sim(self.joint_pos, self.joint_vel) - self._robot.reset() self._robot.set_joint_position_target(self.ctrl_target_joint_pos) # Simulate and update tensors. @@ -652,7 +648,7 @@ def _set_franka_to_default_pose(self, joints, env_ids): def step_sim_no_action(self): """Step the simulation without an action. Used for resets.""" self.scene.write_data_to_sim() - self.sim.step(render=True) + self.sim.step(render=False) self.scene.update(dt=self.physics_dt) self._compute_intermediate_values(dt=self.physics_dt) @@ -753,8 +749,8 @@ def randomize_initial_state(self, env_ids): any_error = torch.logical_or(pos_error, angle_error) bad_envs = bad_envs[any_error.nonzero(as_tuple=False).squeeze(-1)] + # Check IK succeeded for all envs, otherwise try again for those envs if bad_envs.shape[0] == 0: - print("Done IK") break self._set_franka_to_default_pose( @@ -840,11 +836,6 @@ def randomize_initial_state(self, env_ids): self.step_sim_no_action() grasp_time += self.sim.get_physics_dt() - diff = self.target_held_base_pos - self.held_base_pos - - bad_idxs = env_ids[diff[:, 1] > 0.05] - print("Bad:", bad_idxs) - self.prev_joint_pos = self.joint_pos[:, 0:7].clone() self.prev_fingertip_pos = self.fingertip_midpoint_pos.clone() self.prev_fingertip_quat = self.fingertip_midpoint_quat.clone() @@ -887,4 +878,3 @@ def randomize_initial_state(self, env_ids): self._set_gains(self.default_gains) physics_sim_view.set_gravity(carb.Float3(*self.cfg.sim.gravity)) - print("Done Reset") diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py index dd20dec216..0356e9e434 100644 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py +++ b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/factory_env_cfg.py @@ -56,10 +56,12 @@ class CtrlCfg: pos_action_threshold = [0.02, 0.02, 0.02] rot_action_threshold = [0.097, 0.097, 0.097] + reset_joints = [1.5178e-03, -1.9651e-01, -1.4364e-03, -1.9761, -2.7717e-04, 1.7796, 7.8556e-01] reset_task_prop_gains = [300, 300, 300, 20, 20, 20] reset_rot_deriv_scale = 10.0 default_task_prop_gains = [100, 100, 100, 30, 30, 30] + # Null space parameters. default_dof_pos_tensor = [-1.3003, -0.4015, 1.1791, -2.1493, 0.4001, 1.9425, 0.4754] kp_null = 10.0 kd_null = 6.3246 diff --git a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/torch_jit_utils.py b/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/torch_jit_utils.py deleted file mode 100644 index ed57fb6e12..0000000000 --- a/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/factory/torch_jit_utils.py +++ /dev/null @@ -1,39 +0,0 @@ -# Copyright (c) 2022-2024, The Isaac Lab Project Developers. -# All rights reserved. -# -# SPDX-License-Identifier: BSD-3-Clause - -import torch - -import omni.isaac.core.utils.torch as torch_utils - -""" -Rotation conversions -""" - - -@torch.jit.script -def quat_to_angle_axis(q): - # type: (Tensor) -> Tuple[Tensor, Tensor] - # computes axis-angle representation from quaternion q - # q must be normalized - min_theta = 1e-5 - qx, qw = 1, 0 - - sin_theta = torch.sqrt(1 - q[..., qw] * q[..., qw]) - angle = 2 * torch.acos(q[..., qw]) - angle = torch_utils.normalize_angle(angle) - sin_theta_expand = sin_theta.unsqueeze(-1) - axis = q[..., qx:] / sin_theta_expand - - mask = sin_theta > min_theta - default_axis = torch.zeros_like(axis) - default_axis[..., -1] = 1 - - angle = torch.where(mask, angle, torch.zeros_like(angle)) - mask_expand = mask.unsqueeze(-1) - axis = torch.where(mask_expand, axis, default_axis) - return angle, axis - - -# EOF From f01fa941d3407bfcdf9ea62b574ea72014c0546d Mon Sep 17 00:00:00 2001 From: Michael Noseworthy Date: Mon, 16 Dec 2024 13:38:48 -0500 Subject: [PATCH 12/15] Update comprehensive environment list --- docs/source/overview/environments.rst | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/docs/source/overview/environments.rst b/docs/source/overview/environments.rst index 41f52113f4..9fb34aac96 100644 --- a/docs/source/overview/environments.rst +++ b/docs/source/overview/environments.rst @@ -402,6 +402,18 @@ Comprehensive List of Environments - - Manager Based - **rl_games** (PPO), **rsl_rl** (PPO), **skrl** (PPO), **sb3** (PPO) + * - Isaac-Factory-GearMesh-Direct-v0 + - + - Direct + - **rl_games** (PPO) + * - Isaac-Factory-NutThread-Direct-v0 + - + - Direct + - **rl_games** (PPO) + * - Isaac-Factory-PegInsert-Direct-v0 + - + - Direct + - **rl_games** (PPO) * - Isaac-Franka-Cabinet-Direct-v0 - - Direct From 59cdba12f06c4e56f58e5b97dfb82d08bd5f5d2c Mon Sep 17 00:00:00 2001 From: Kelly Guo Date: Mon, 16 Dec 2024 16:18:36 -0500 Subject: [PATCH 13/15] Update extension.toml Signed-off-by: Kelly Guo --- source/extensions/omni.isaac.lab_tasks/config/extension.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/source/extensions/omni.isaac.lab_tasks/config/extension.toml b/source/extensions/omni.isaac.lab_tasks/config/extension.toml index accf06c5c5..e82e19521f 100644 --- a/source/extensions/omni.isaac.lab_tasks/config/extension.toml +++ b/source/extensions/omni.isaac.lab_tasks/config/extension.toml @@ -1,7 +1,7 @@ [package] # Note: Semantic Versioning is used: https://semver.org/ -version = "0.10.15" +version = "0.10.16" # Description title = "Isaac Lab Environments" From a2bc64129a208383428646797743b2f135367bbc Mon Sep 17 00:00:00 2001 From: Kelly Guo Date: Mon, 16 Dec 2024 17:42:36 -0500 Subject: [PATCH 14/15] Update per_test_timeouts.py Signed-off-by: Kelly Guo --- tools/per_test_timeouts.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/per_test_timeouts.py b/tools/per_test_timeouts.py index a82b49daac..c0581f5f13 100644 --- a/tools/per_test_timeouts.py +++ b/tools/per_test_timeouts.py @@ -10,7 +10,7 @@ PER_TEST_TIMEOUTS = { "test_articulation.py": 200, "test_deformable_object.py": 200, - "test_environments.py": 1200, # This test runs through all the environments for 100 steps each + "test_environments.py": 1250, # This test runs through all the environments for 100 steps each "test_environment_determinism.py": 200, # This test runs through many the environments for 100 steps each "test_env_rendering_logic.py": 300, "test_camera.py": 500, From ce0f3855be882fd1b86ad932b3c06401ff4b3404 Mon Sep 17 00:00:00 2001 From: Kelly Guo Date: Mon, 16 Dec 2024 19:19:58 -0500 Subject: [PATCH 15/15] Update per_test_timeouts.py Signed-off-by: Kelly Guo --- tools/per_test_timeouts.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/per_test_timeouts.py b/tools/per_test_timeouts.py index c0581f5f13..340abde85c 100644 --- a/tools/per_test_timeouts.py +++ b/tools/per_test_timeouts.py @@ -10,7 +10,7 @@ PER_TEST_TIMEOUTS = { "test_articulation.py": 200, "test_deformable_object.py": 200, - "test_environments.py": 1250, # This test runs through all the environments for 100 steps each + "test_environments.py": 1500, # This test runs through all the environments for 100 steps each "test_environment_determinism.py": 200, # This test runs through many the environments for 100 steps each "test_env_rendering_logic.py": 300, "test_camera.py": 500,

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