PatternDex: Learning Interaction Patterns to Guide Reinforcement Learning of Bimanual Dexterous Manipulation of Articulated Objects

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为 VLA、具身智能、人形机器人控制、机器人操作等高价值论文补充中文说明。

Original abstract

In this paper, we develop a method that enables bimanual dexterous hands to manipulate articulated objects with a high success rate without suffering from an embodiment gap. We observe that the correlation between hand motions and object motions is dictated by the object rather than the hands and can be learned from human-object demonstrations. Based on this observation, we propose PatternDex, a method that learns this correlation and represents it as a token sequence, which we call an interaction pattern. From this pattern, PatternDex estimates the wrist motions and contact points that fit the target robot, and then trains a reinforcement learning policy that exploits these estimates as guidance. Since the guidance fits the target embodiment, the policy explores only the actions that the target robot can execute and thus achieves high success rates. PatternDex also requires only simple fine-tuning to train a new robot, since it can reuse the learned interaction pattern. We evaluate PatternDex with bimanual dexterous hands on human demonstrations from the ARCTIC dataset. PatternDex achieves, on average, a 92.8% success rate with Allegro hands, while the state-of-the-art baseline achieves 52.2%. Also, it achieves success rates above 70% with three other robot hands after fine-tuning alone. Furthermore, we verify that the learned policy transfers well to a real-world task of opening a microwave. Videos and additional results are available at https://patterndex.github.io/PatternDex/

Links and sources

• Official / arXiv page
• PDF from original source

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