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AnySkill: Learning Open-Vocabulary Physical Skill for Interactive Agents (CVPR 2024)

Paper arXiv Project Page Video Youtube Checkpoints

AnySkill, a novel hierarchical method that learns physically plausible interactions following open-vocabulary instructions.

TODOs

  • Release training code.
  • Release the model of low-level controller.

Installation

Download Isaac Gym from the website, then follow the installation instructions.

Once Isaac Gym is installed, install the external dependencies for this repo:

pip install -r requirements.txt

Low-level controller training

[NEW] We have provided our well-trained model of low-level controller, you can download from this link.

First, a CALM model can be trained to imitate a dataset of motions clips using the following command:

python calm/run.py
--task HumanoidAMPGetup
--cfg_env calm/data/cfg/humanoid.yaml
--cfg_train ./calm/data/cfg/train/rlg/calm_humanoid.yaml
--motion_file [Your file path]/motions.yaml
--track

--motion_file can be used to specify a dataset of motion clips that the model should imitate. The task HumanoidAMPGetup will train a model to imitate a dataset of motion clips and get up after falling. Over the course of training, the latest checkpoint Humanoid.pth will be regularly saved to output/, along with a Tensorboard log. --headless is used to disable visualizations and --track is used for tracking using weights and biases. If you want to view the simulation, simply remove this flag. To test a trained model, use the following command:

Test the trained low-level controller model

python calm/run.py
--test
--task HumanoidAMPGetup
--num_envs 16
--cfg_env calm/data/cfg/humanoid.yaml
--cfg_train calm/data/cfg/train/rlg/calm_humanoid.yaml
--motion_file [Your file path]/motions.yaml
--checkpoint [Your file path]/Humanoid_00014500.pth

 

High-level policy

High-level policy training

python calm/run.py
--task HumanoidSpecAnySKill
--cfg_env calm/data/cfg/humanoid_anyskill.yaml
--cfg_train calm/data/cfg/train/rlg/spec_anyskill.yaml
--motion_file [Your file path]/motions.yaml
--llc_checkpoint [Your file path]/Humanoid_00014500.pth
--track
--text_file calm/data/texts.yaml
--wandb_project_name special_policy
--render

--llc_checkpoint specifies the checkpoint to use for the low-level controller. --text_file specifies motion captions and their weights. For both training method, we use pretrained model to extract the image features by default. If you want to render with camera, you just need add --render at the end.

Test the trained high-level model

python calm/run.py 
--test
--num_envs 16
--task HumanoidSpecAnySKill
--cfg_env calm/data/cfg/humanoid_anyskill.yaml
--cfg_train calm/data/cfg/train/rlg/spec_anyskill.yaml
--motion_file [Your file path]/motions.yaml
--llc_checkpoint [Your file path]/Humanoid_00014500.pth
--track
--render
--text_file calm/data/texts.yaml
--checkpoint [Your file path]/Humanoid_00000100.pth

--checkpoint here is the trained model with high-level policy.

Rigid scene policy training

python calm/run.py
--task HumanoidSpecAnySKillRigid
--cfg_env calm/data/cfg/humanoid_anyskill.yaml
--cfg_train calm/data/cfg/train/rlg/spec_anyskill.yaml
--motion_file [Your file path]/motions.yaml
--llc_checkpoint [Your file path]/Humanoid_00014500.pth
--track
--text_file calm/data/texts_rigid.yaml
--wandb_project_name special_policy_scene
--render

You can replace --cfg_train and --text_file with your own files.

Test the model trained with rigid object

python calm/run.py 
--test
--num_envs 16
--task HumanoidSpecAnySKillRigid
--cfg_env calm/data/cfg/humanoid_anyskill.yaml
--cfg_train calm/data/cfg/train/rlg/spec_anyskill.yaml
--motion_file [Your file path]/motions.yaml
--llc_checkpoint [Your file path]/Humanoid_00014500.pth
--track
--render
--text_file calm/data/texts_rigid.yaml
--checkpoint [Your file path]/Humanoid_00000050.pth

Articulated scene policy training

python calm/run.py
--task HumanoidSpecAnySKillArti
--cfg_env calm/data/cfg/humanoid_anyskill.yaml
--cfg_train calm/data/cfg/train/rlg/scene_anyskill.yaml
--motion_file [Your file path]/motions.yaml
--llc_checkpoint [Your file path]/Humanoid_00014500.pth
--track
--text_file calm/data/texts_scene.yaml
--wandb_project_name special_policy_scene
--articulated
--render

Here we add --articulated to specify the articulated object in the scene.

Test the model trained with articulated object

python calm/run.py 
--test
--num_envs 16
--task HumanoidSpecAnySKillArti
--cfg_env calm/data/cfg/humanoid_anyskill.yaml
--cfg_train calm/data/cfg/train/rlg/scene_anyskill.yaml
--motion_file [Your file path]/motions.yaml
--llc_checkpoint [Your file path]/Humanoid_00014500.pth
--track
--render
--articulated
--text_file calm/data/texts_scene.yaml
--checkpoint [Your file path]/Humanoid_00000100.pth

 

Motion Data

Motion clips are located in calm/data/motions/. Individual motion clips are stored as .npy files. Motion datasets are specified by .yaml files, which contains a list of motion clips to be included in the dataset. Motion clips can be visualized with the following command:

python calm/run.py
--test
--task HumanoidViewMotion
--num_envs 1
--cfg_env calm/data/cfg/humanoid.yaml
--cfg_train calm/data/cfg/train/rlg/amp_humanoid.yaml
--motion_file [Your file path].npy

--motion_file can be used to visualize a single motion clip .npy or a motion dataset .yaml. If you want to retarget new motion clips to the character, you can take a look at an example retargeting script in calm/poselib/retarget_motion.py.

Acknowledgments

Our code is based on CALM and CLIP. Thanks for these great projects.

Citation

@inproceedings{cui2024anyskill,
  title={Anyskill: Learning Open-Vocabulary Physical Skill for Interactive Agents},
  author={Cui, Jieming and Liu, Tengyu and Liu, Nian and Yang, Yaodong and Zhu, Yixin and Huang, Siyuan},
  booktitle=Conference on Computer Vision and Pattern Recognition(CVPR),
  year={2024}
}