LETS: Low-Overhead External-Torque Sensing for Force-Feedback Bilateral Teleoperation
LETS: Low-Overhead External-Torque Sensing for Force-Feedback Bilateral Teleoperation
This research proposes LETS, a physics-guided learning approach for external-torque estimation in bilateral teleoperation. It uses only a robot's URDF model and joint feedback, avoiding the need for end-effector force/torque sensors. LETS achieves real-time torque estimation with 69% lower free-space error and 28% lower contact error than manufacturer estimates, improving haptic feedback and reducing oscillations between leader and follower.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
training time
<1 minutes
free-motion data
12 minutes
training duration
<1 minutes
free-motion data duration
12 minutes
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
cated 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.
Open source S4
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 dedi
Open source S3
Source：arXiv Robotics — research abstracts · arxiv.org