通用操控外骨骼：利用实时扭矩反馈学习全身柔顺策略
Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback
AI总结：
提出通用操控外骨骼（UME），通过实时触觉扭矩反馈和全身数据采集，使机器人学习主动柔顺策略，在受限空间中完成移动操作、力控翻转等任务。
摘要：
为了使机器人在家庭环境中安全工作，它们需要具备柔顺性，并在接触过程中对扭矩和力反馈做出反应。然而，现有的大多数数据采集管道仍然缺乏捕捉力和扭矩数据以学习主动柔顺策略的能力。在本文中，我们提出了通用操控外骨骼（UME），一种上肢外骨骼，它提供实时触觉扭矩反馈，同时记录整个手臂的配置和关节扭矩信号用于遥操作。凭借透明的扭矩反馈，人类操作员甚至可以在蒙眼的情况下拔出运动学约束的物体。UME成本低、重量轻且便携。配备嵌入式IMU，它支持移动操作的遥操作。通过我们提出的通用重定向算法，UME可以遥操作多种机器人，包括7自由度OpenArm、7自由度Franka和6自由度X-ARM。我们证明，这些能力的组合使得学习双臂、全身和主动柔顺策略成为可能，这些策略在高度受限的空间中有效运行。学习到的鲁棒自主策略在各种任务中实现了高成功率，包括长时程移动操作、力介导的箱子翻转、视觉遮挡的箱子推挤以及空间受限的桌面操作。视频、代码和更多信息可在此https URL找到。
英文摘要：
For robots to work safely in household environments, they need to be compliant and react to torque and force feedback during contact. However, the majority of existing data collection pipelines still lack the ability to capture force and torque data for learning active compliant policies. In this paper, we present Universal Manipulation Exoskeleton (UME), an upper-limb exoskeleton that provides real-time haptic torque feedback while recording whole-arm configurations and joint torque signals for teleoperation. With transparent torque feedback, human operators can even unsheathe kinematically constrained objects while blindfolded. UME is low-cost, lightweight, and portable. Equipped with an embedded IMU, it enables teleoperation for mobile manipulation. With our proposed universal retargeting algorithm, UME can teleoperate a range of robots, including the 7DoF OpenArm, 7DoF Franka, and 6DoF X-ARM. We demonstrate that this combination of capabilities enables learning bimanual, whole-body, and active compliant policies that operate effectively in highly constrained spaces. The learned robust autonomous policies achieve high success rates across a variety of tasks, including long-horizon mobile manipulation, force-mediated box flipping, visually occluded box pushing, and space-constrained tabletop manipulation. Videos, code, and additional information can be found at https://ume-exo.github.io.
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。
