LETS: Low-Overhead External-Torque Sensing for Force-Feedback Bilateral Teleoperation
Authors: Zonghao Guo, Dongchen Guan, Yubo Wang, Peng Yang
Organizations: Institute of Automation, Chinese Academy of Sciences, China. · School of Artificial Intelligence, University of Chinese Academy of Sciences, China. · Beijing Amigos Robot Technology Co., Ltd., China.
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.
Figures & tables
Fig. 1: System overview: (a) bilateral platform, (b) bimanual box manipulation, and (c) single-arm contact interaction. Red arrows denote force feedback; blue arrows denote motion commands.
Fig. 2: LETS online system architecture. Fixed RNEA uses the URDF and current joint state, while the frozen GRU uses joint-state history. Their predictions are subtracted from measured joint torque to estimate external torque at 200 Hz. The estimate is mapped and scaled for leader-side force feedback; leader motion commands drive the follower, using joint impedance control in our experiments. Control commands do not enter the LETS estimator.
Protocol
Runs
Reference
Role
Train/validation/test
8/1/1
τext=0
fitting/selection/locked test
Subsequent free space
9
τext=0
frozen primary evaluation
F/T-referenced contact
9
τFT
frozen primary evaluation
Second physical unit
7/1/1
τext=0
fixed-protocol replication
Compound shift
10 poses
τFT
robustness stress test
TABLE I: Experimental protocols and their roles.
Fig. 3: Feedback-sensitive conditions: (a) free-space bilateral operation and (b) rigid contact with an independently measured end-effector wrench.
Estimator
Free space
Contact ( τFT )
Manufacturer estimate
0.54
0.57
Nominal RNEA
0.53
0.55
Nominal GMO
0.51
0.69
Calibrated GMO
0.17
0.57
Pure learning
0.35
0.54
LETS
0.17
0.41
TABLE II: Run-equal joint-torque RMSE ( N⋅m ) over nine frozen runs.
Fig. 4: Representative Joint 1 time-domain estimates from the frozen evaluation runs. (a) Free-space motion, where the external-torque reference is zero. (b) Rigid contact, where the reference is obtained independently from the six-axis F/T sensor as τFT .
Fig. 5: Controlled factorization study. (a) Mean per-joint test RMSE under a fixed NEXT architecture. (b) Uniform within-run subsampling demonstrates that 10% of physics-guided training windows outperform all pure-learning windows. (c) Run-equal contact RMSE for validation-selected models over nine runs using the independent joint-space reference τFT .
Method
RMSE
MAE
( N⋅m )
( N⋅m )
Manufacturer raw output
0.64
0.53
Nominal RNEA
0.56
0.48
NEXT direct torque
0.46
0.34
RNEA + NEXT residual
0.24
0.17
TABLE III: Fixed-architecture NEXT torque-estimation results ( N⋅m ).
Condition
LETS
Command-aware LETS
Free space
0.17
0.21
Contact
0.41
0.42
Distribution shift
0.52
0.59
TABLE IV: LETS and command-aware input ablation at 200 Hz (RMSE in N⋅m ).
Fig. 6: Compound-shift RMSE over ten held-out poses. Each cell reports the mean per-joint RMSE for one pose; lower is better. LETS is lowest in every pose-wise comparison.
Fig. 7: Real-robot closed-loop stress test with intermediate trajectory smoothing bypassed. Successive frames progress from left to right. Red denotes observed unloaded oscillation; green denotes smooth free motion or stable rigid contact. Continuous trajectories are provided in the supplementary video.
Robot teleoperation studies how people remotely control robots through interfaces, sensing, feedback, and autonomy, despite distance, delay, and embodiment gaps.