从人类演示中学习多样化的双手灵巧操作技能
Learning Diverse Bimanual Dexterous Manipulation Skills from Human Demonstrations
AI总结：
本文提出BiDexHD框架，通过从现有双手数据集自动构建任务并结合教师-学生策略学习，在TACO数据集141个任务上实现了74.59%的已训练任务完成率和51.07%的零样本泛化能力，推动了通用双手灵巧操作技能的学习。
摘要：
双手灵巧操作是机器人学中一个关键但尚未得到充分探索的领域。其高维动作空间和固有的任务复杂性给策略学习带来了重大挑战，而现有基准中有限的任务多样性也阻碍了通用技能的发展。现有方法在很大程度上依赖强化学习，通常受限于为狭窄任务集精心设计的奖励函数。在这项工作中，我们提出了一种新方法，用于从大量人类演示中高效学习多样化的双手灵巧技能。具体来说，我们引入了BiDexHD，这是一个统一从现有双手数据集中构建任务并采用教师-学生策略学习来处理所有任务的框架。教师利用通用的两阶段奖励函数在具有共享行为的任务中学习基于状态的策略，而学生则将学到的多任务策略蒸馏为基于视觉的策略。借助BiDexHD，从自动构建的任务中可扩展地学习大量双手灵巧技能变得可行，为通向通用双手灵巧操作提供了有前景的进展。我们在TACO数据集上进行了实证评估，该数据集涵盖六个类别的141个任务，结果表明在已训练任务上的任务完成率为74.59%，在未见任务上为51.07%，展示了BiDexHD的有效性和具有竞争力的零样本泛化能力。有关视频和更多信息，请访问我们的项目页面 https://sites.google.com/view/bidexhd。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
