REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous ManipulationDownload PDF

Published: 30 Aug 2023, Last Modified: 19 Oct 2023CoRL 2023 PosterReaders: Everyone
Keywords: Dexterous Manipulation, Reinforcement Learning, Sample-Efficient RL
TL;DR: We learn dexterous manipulation skills on multi-fingered robot hand in the real world autonomously via sample efficient RL by bootstrapping from prior data
Abstract: 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. \href{}{})
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