Universal Manipulation Exoskeleton

Universal Manipulation ExoskeletonLearning Compliant Whole-body Policies

with Real-time Torque Feedback

Litian Liang*, 1,<br>James (Jingxi) Xu*, 1, 2,<br>Xinda Qi1,<br>Yujun Cai1,<br>Houzhu Ding1,<br>Luqi Wang1,<br>Zhixin Sun1,

Jyh-Herng Chow1,<br>Ming Yang1,<br>Mark Cutkosky2

1Ant Group,<br>2Stanford University

*Equal contribution

Paper

arXiv

Video

Code (coming soon)

Data<br>-->

Teleoperator unsheathing a metal sword relying purely on real-time haptic torque feedback.

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<br>pipelines still lack the ability to capture force and torque data for learning active compliant policies. In this paper, we present<br>Universal Manipulation Exoskeleton (UME), an upper-limb exoskeleton that provides real-time haptic torque feedback while recording whole-arm configurations<br>and joint torque signals for teleoperation. With transparent torque feedback, human operators can even unsheathe kinematically constrained objects while blindfolded. UME is low-cost,<br>lightweight, and portable. Equipped with an embedded IMU, it enables teleoperation for mobile manipulation. With our proposed universal retargeting algorithm, UME can teleoperate a<br>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<br>compliant policies that operate effectively in highly constrained spaces. The learned robust autonomous policies achieve high success rates across a variety of tasks, including<br>long-horizon mobile manipulation, force-mediated box flipping, visually occluded box pushing, and space-constrained tabletop manipulation.

Video

Autonomous Policy Demonstrations

All policies are trained soley on data collected from UME

We study UME's ability to collect data that enables successful learning of autonomous, compliant, bimanual, and whole-body robot policies. All autonomous policies are demonstrated on our<br>in-house-built dual-arm mobile manipulator.

Whole-Body Fridge Drink Retrieval

(Autonomous)

Task Definition: The robot initially faces the table and checks whether a drink can is present. If no drink is detected, the robot turns around, opens the fridge door, picks up a drink from the side<br>holder of the door, transports it while using the other gripper to support the bottom of the can, and places it on the table. If the drink is removed afterward, the robot repeats the same<br>procedure, ensuring that a drink can is always available on the table. Note that the can is unopened and full, not empty.

Challenges: Whole-body mobile manipulation. Long-horizon.

Drink retrieval after the existing one is taken away

Robustness to disturbance

Comparison with UMI

Comparison with No-torque

Space-Constrained GPU Picking

(Autonomous)

Task Definition: The robot must reach between two desktops to pick up a GPU card located at the back of the constrained space, and retrieve it to a location outside the enclosure.

Challenges: Highly space-constrained. Force-mediated. Requires whole-body movement.

GPU picking from highly constrained space

Comparison with UMI

Force-Mediated Box Flipping

(Autonomous)

Task Definition: Push a box against a vertically fixed surface to flip the box upright.

Challenges: Highly force-mediated.

Force-mediated box flipping

Comparison with No-torque

Visually Occluded Box Pushing

(Autonomous)

Task Definition: Push a target box into the space between two desktop PCs until it reaches the very end, leaving no gap between the box and any hidden objects behind it. This task is inspired by how<br>humans clean tabletops by shuffling objects all the way against the wall.

Challenges: Visually occluded. Highly force-mediated.

Push the box all the way to the end

Comparison with UMI

Universally Teleoperating Other Arms

6DoF X-ARMs

7DoF Franka Arms in Simulation

Range of Motion (RoM) & Gravity Compensation

Range of Motion (RoM)

Gravity Compensation

Real-time Transparent Torque Feedback

Teleoperator Feeling the Torque Feedback When Blindfolded

Unsheathing a Metal Sword Relying on Torque Feedback

Robot Teleoperation

Dual-arm Teleoperation (1x)

Mobile Base Teleoperation (1x)

Data Collection & User Study

Collecting Mobile Manipulation Demonstrations

User Study

BibTeX

@article{liang2026universalmanipulationexoskeletonlearning,<br>title={Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback},<br>author={Litian Liang and Jingxi Xu and Xinda Qi and Yujun Cai and Houzhu Ding and Luqi Wang and Zhixin Sun and Jyh-Herng Chow and Ming Yang and Mark Cutkosky},<br>year={2026},<br>eprint={2606.14218},<br>archivePrefix={arXiv},<br>primaryClass={cs.RO},<br>url={https://arxiv.org/abs/2606.14218},

Questions & Answers

What is the cost of UME?

The entire UME system costs $1900. A detailed...