Learning Diverse Bimanual Dexterous Manipulation Skills from Human Demonstrations

• URL: http://arxiv.org/abs/2410.02477v1
• Date: Thu, 3 Oct 2024 13:35:15 GMT
• Title: Learning Diverse Bimanual Dexterous Manipulation Skills from Human Demonstrations
• Authors: Bohan Zhou, Haoqi Yuan, Yuhui Fu, Zongqing Lu,
• Abstract summary: BiDexHD is a framework that unifies task construction from existing bimanual datasets and employs teacher-student policy learning to address all tasks. Our empirical evaluation on the TACO dataset, spanning 141 tasks across six categories, demonstrates a task fulfillment rate of 74.59% on trained tasks and 51.07% on unseen tasks.
• Score: 29.867524903691802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bimanual dexterous manipulation is a critical yet underexplored area in robotics. Its high-dimensional action space and inherent task complexity present significant challenges for policy learning, and the limited task diversity in existing benchmarks hinders general-purpose skill development. Existing approaches largely depend on reinforcement learning, often constrained by intricately designed reward functions tailored to a narrow set of tasks. In this work, we present a novel approach for efficiently learning diverse bimanual dexterous skills from abundant human demonstrations. Specifically, we introduce BiDexHD, a framework that unifies task construction from existing bimanual datasets and employs teacher-student policy learning to address all tasks. The teacher learns state-based policies using a general two-stage reward function across tasks with shared behaviors, while the student distills the learned multi-task policies into a vision-based policy. With BiDexHD, scalable learning of numerous bimanual dexterous skills from auto-constructed tasks becomes feasible, offering promising advances toward universal bimanual dexterous manipulation. Our empirical evaluation on the TACO dataset, spanning 141 tasks across six categories, demonstrates a task fulfillment rate of 74.59% on trained tasks and 51.07% on unseen tasks, showcasing the effectiveness and competitive zero-shot generalization capabilities of BiDexHD. For videos and more information, visit our project page https://sites.google.com/view/bidexhd.

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