Open Access

ARTICLE

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

Guangyu Xu^1,2, Yuxin Liu¹, Bo Yang¹, Siyu Lu^3,*, Chao Liu⁴, Junmin Lyu⁵, Wenfeng Zheng^1,*

1 School of Automation, University of Electronic Science and Technology of China, Chengdu, 611731, China
2 School of the Environment, The University of Queensland, Brisbane St Lucia 2, Brisbane, QLD 4072, Australia
3 Department of Geography, Texas A&M University, College Station, TX 77843, USA
4 Department of Robotics, LIRMM, University of Montpellier—CNRS, Montpellier, 34095, France
5 School of Artificial Intelligence, Guangzhou Huashang University, Guangzhou, 511300, China

* Corresponding Authors: Siyu Lu. Email: ; Wenfeng Zheng. Email:

(This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)

Computer Modeling in Engineering & Sciences 2026, 146(1), 34 https://doi.org/10.32604/cmes.2025.074938

Received 21 October 2025; Accepted 02 December 2025; Issue published 29 January 2026

Abstract

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.

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Keywords

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