Learning-Based Prediction of Soft-Tissue Motion for Latency Compensation in Teleoperation

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.