Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback

2026-06-12

AI Summary

Current robotic teleoperation systems struggle to provide force/torque feedback, limiting the deployment of active compliance strategies in complex domestic environments. To address this challenge, this work proposes UME—a low-cost, lightweight, and portable upper-limb exoskeleton that integrates real-time torque-based haptic feedback with full-body motion capture, enabling transparent teleoperation for blind manipulation of constrained objects for the first time. UME employs a universal retargeting algorithm to interface seamlessly with diverse heterogeneous robotic arms and leverages a hybrid learning framework combining imitation and reinforcement learning to train whole-body compliant control policies. Experimental results demonstrate high success rates across a range of challenging tasks, including long-duration mobile manipulation, force-controlled box flipping, visually occluded pushing, and dexterous operations in confined desktop spaces.

Abstract

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.

Problem

• compliant manipulation
• torque feedback
• force feedback
• teleoperation
• whole-body control

Innovation

• exoskeleton teleoperation
• real-time torque feedback
• active compliance
• universal retargeting
• mobile manipulation