2023-12-02-hu23a.md

title	section	openreview	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation	Poster	ckeT8cMz_A	Dexterous manipulation tasks involving contact-rich interactions pose a significant challenge for both model-based control systems and imitation learning algorithms. The complexity arises from the need for multi-fingered robotic hands to dynamically establish and break contacts, balance forces on the non-prehensile object, and control a high number of degrees of freedom. Reinforcement learning (RL) offers a promising approach due to its general applicability and capacity to autonomously acquire optimal manipulation strategies. However, its real-world application is often hindered by the necessity to generate a large number of samples, reset the environment, and obtain reward signals. In this work, we introduce an efficient system for learning dexterous manipulation skills with RL to alleviate these challenges. The main idea of our approach is the integration of recent advancements in sample-efficient RL and replay buffer bootstrapping. This unique combination allows us to utilize data from different tasks or objects as a starting point for training new tasks, significantly improving learning efficiency. Additionally, our system completes the real-world training cycle by incorporating learned resets via an imitation-based pickup policy and learned reward functions, to eliminate the need for manual reset and reward engineering. We show the benefits of reusing past data as replay buffer initialization for new tasks, for instance, the fast acquisitions of intricate manipulation skills in the real world on a four-fingered robotic hand. https://sites.google.com/view/reboot-dexterous	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	hu23a	0	REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation	1930	1949	1930-1949	1930	false	Hu, Zheyuan and Rovinsky, Aaron and Luo, Jianlan and Kumar, Vikash and Gupta, Abhishek and Levine, Sergey	given family Zheyuan Hu given family Aaron Rovinsky given family Jianlan Luo given family Vikash Kumar given family Abhishek Gupta given family Sergey Levine	2023-12-02		Proceedings of The 7th Conference on Robot Learning	229	inproceedings	date-parts 2023 12 2	https://proceedings.mlr.press/v229/hu23a/hu23a.pdf