LETS: Low-Overhead External-Torque Sensing for Force-Feedbac
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: 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/to…
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Reported quantity · training time: 1 other · Basis not reported
“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”
Exact source · revision 1Source owner not reported
Reported quantity · free-motion data: 12 other · Basis not reported
“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”
Exact source · revision 1Source owner not reported
Reported quantity · training duration: 1 other · Basis not reported
“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”
Exact source · revision 1Source owner not reported
Reported quantity · free-motion data duration: 12 other · Basis not reported
“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”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsLETS: 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.
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