ScrewMimic：基于螺旋空间投影从人类视频中进行双臂模仿
ScrewMimic: Bimanual Imitation from Human Videos with Screw Space Projection
AI总结：
ScrewMimic提出将双手交互建模为螺旋运动并定义螺旋动作空间，使机器人能从单个人类视频演示中学习双臂操作并通过自监督微调，性能优于在原始运动空间中学习的基线方法。
摘要：
双臂操作长期以来是机器人领域的一项挑战，原因在于其自由度数量庞大，以及生成有意义行为所需的严格空间与时间同步。人类通过观察他人并借助游戏来精炼自身能力，从而习得双臂操作技能。在本工作中，我们旨在使机器人能够从人类视频演示中学习双臂操作行为，并通过交互对其进行微调。受心理学与生物力学中开创性工作的启发，我们提出将双手之间的交互建模为一种串联运动学链接——特别是作为一种螺旋运动，并以此定义一个新的双臂操作动作空间：螺旋动作（screw actions）。我们引入了 ScrewMimic，一个利用这种新颖动作表示来促进从人类演示中学习以及自监督策略微调的框架。我们的实验表明，ScrewMimic 能够从单个人类视频演示中学习多种复杂的双臂行为，并且其性能优于那些直接在双臂原始运动空间中解释演示并进行微调的基线方法。更多信息和视频结果见 https://robin-lab.cs.utexas.edu/ScrewMimic/。
英文摘要：
Bimanual manipulation is a longstanding challenge in robotics due to the large number of degrees of freedom and the strict spatial and temporal synchronization required to generate meaningful behavior. Humans learn bimanual manipulation skills by watching other humans and by refining their abilities through play. In this work, we aim to enable robots to learn bimanual manipulation behaviors from human video demonstrations and fine-tune them through interaction. Inspired by seminal work in psychology and biomechanics, we propose modeling the interaction between two hands as a serial kinematic linkage -- as a screw motion, in particular, that we use to define a new action space for bimanual manipulation: screw actions. We introduce ScrewMimic, a framework that leverages this novel action representation to facilitate learning from human demonstration and self-supervised policy fine-tuning. Our experiments demonstrate that ScrewMimic is able to learn several complex bimanual behaviors from a single human video demonstration, and that it outperforms baselines that interpret demonstrations and fine-tune directly in the original space of motion of both arms. For more information and video results, https://robin-lab.cs.utexas.edu/ScrewMimic/
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。
