TY  - EJOU
AU  - Xu, Guangyu 
AU  - Liu, Yuxin 
AU  - Yang, Bo 
AU  - Lu, Siyu 
AU  - Liu, Chao 
AU  - Lyu, Junmin 
AU  - Zheng, Wenfeng 

TI  - Learning-Based Prediction of Soft-Tissue Motion for Latency Compensation in Teleoperation
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2026
VL  - 146
IS  - 1
SN  - 1526-1506

AB  - Soft-tissue motion introduces significant challenges in robotic teleoperation, especially in medical scenarios where precise target tracking is critical. Latency across sensing, computation, and actuation chains leads to degraded tracking performance, particularly around high-acceleration segments and trajectory inflection points. This study investigates machine learning-based predictive compensation for latency mitigation in soft-tissue tracking. Three models—autoregressive (AR), long short-term memory (LSTM), and temporal convolutional network (TCN)—were implemented and evaluated on both synthetic and real datasets. By aligning the prediction horizon with the end-to-end system delay, we demonstrate that prediction-based compensation significantly reduces tracking errors. Among the models, TCN achieved superior robustness and accuracy on complex motion patterns, particularly in multi-step prediction tasks, and exhibited better latency–horizon compatibility. The results suggest that TCN is a promising candidate for real-time latency compensation in teleoperated robotic systems involving dynamic soft-tissue interaction.
KW  - Medical robotics; teleoperation; soft tissue tracking; motion prediction; real-time control

DO  - 10.32604/cmes.2025.074938