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arXiv · 2610.14889

LETS: Low-Overhead External-Torque Sensing for Force-Feedback Bilateral Teleoperation

Zonghao Guo·Dongchen Guan·Yubo Wang·Peng Yang
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Abstract

Bilateral teleoperation with force feedback is becoming increasingly important, and the quality of external-torque estimation is critical to this process. However, manufacturer-provided disturbance-torque estimates on commercial robotic arms are often inaccurate, while the use of end-effector force/torque sensors adds cost and payload. We propose LETS, a physics-guided learning approach that assigns rigid-body dynamics to recursive Newton-Euler inverse dynamics and reserves learning for only the robot-specific residual. LETS uses only the robot's URDF model and joint feedback, deliberately excluding control commands and thereby decoupling external-torque estimation from control-command inputs. It trains on 12 minutes of free-motion data in under one minute, without requiring a dedicated end-effector six-axis force/torque sensor. In bilateral teleoperation, LETS achieves real-time external-torque estimation that enables stable force feedback. Its free-space error is 69% lower than the manufacturer-provided estimate, substantially reducing coupled oscillations between leader and follower; its contact error is 28% lower, yielding more accurate haptic feedback.

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Zonghao Guo, Dongchen Guan, Yubo Wang, Peng Yang. 2026-10-10. LETS: Low-Overhead External-Torque Sensing for Force-Feedback Bilateral Teleoperation. https://arxiv.org/abs/2610.14889

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