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.

翻译：为使机器人在家庭环境中安全作业，需具备接触过程中的柔顺性及对力矩与力反馈的响应能力。然而，现有大多数数据采集流程仍无法有效捕获力与力矩数据以学习主动柔性策略。本文提出通用操控外骨骼（UME）——一种能够提供实时触觉力矩反馈的上肢外骨骼系统，可在遥操作过程中记录全臂构型与关节力矩信号。基于透明力矩反馈，人类操作员甚至可在蒙眼状态下完成运动学约束物体的解鞘操作。UME具有低成本、轻量化及便携特性，通过内置惯性测量单元（IMU）可实现移动操纵遥操作。借助所提出的通用重定向算法，UME可操控包括7自由度OpenArm、7自由度Franka及6自由度X-ARM在内的多种机器人。实验表明，该组合能力支持学习在高度受限空间中有效运行的双臂、全身及主动柔性策略。所学习的鲁棒自主策略在长时域移动操控、力介导箱体翻转、视觉遮挡箱体推拉及空间受限桌面操控等多种任务中均实现高成功率。相关视频、代码及详细信息见https://ume-exo.github.io。