Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback
For robotics researchers, this work addresses the lack of torque feedback in data collection for learning compliant policies, but the approach is incremental as it combines existing exoskeleton and teleoperation techniques.
The paper introduces UME, a low-cost upper-limb exoskeleton that provides real-time torque feedback for teleoperation, enabling learning of compliant whole-body policies. The learned policies achieve high success rates across tasks like mobile manipulation and force-mediated box flipping.
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.