A VisuoMotor Human-Robot Interaction Framework for Attention-Motion-Integrated Training
Chen Chen, Shuhe Yuan, Jingting Zhang, Fengjun Mu, Chaobin Zou, Hong Cheng
Abstract
Focus of attention is one of the most influential factors facilitating motor training performance. Most of robotic training methods have not well solved the negative effect of divided-attention on motor execution performance, resulting in limited rehabilitation efficiency for motor-cognitive dysfunction. In this study, we propose a novel visuomotor human-robot interaction framework by integrating a gaze-visual game and force-movement robot, to realize more efficient training for both attentional and motor function. An important novelty of this framework is to design a dynamical pattern recognition scheme for the hierarchical-coupled behavior of attentional and motor execution, to facilitate efficient human-robot interaction in both cognitive and motor perspectives. Specifically, an attentional-motor dynamical system modeling method is first developed by using the gaze, force and movement data collected from the human under different attentional-motor behavior. Then, an online dynamical pattern recognition scheme can be design with these models to online recognizing the human’s attentional and motor behavior states. The training robot system can dynamically adjust the parameters according to the recognition results, to guide the collaboration of both attentional and motor training. Experimental study are conducted to demonstrate the desired accuracy and efficiency of our designed approaches in attentional-motor behavior recognition and training.
BibTeX
@inproceedings{iros2025_avisuomotorhuman,
title = {A VisuoMotor Human-Robot Interaction Framework for Attention-Motion-Integrated Training},
author = {Chen Chen and Shuhe Yuan and Jingting Zhang and Fengjun Mu and Chaobin Zou and Hong Cheng},
booktitle = {IROS 2025},
year = {2025}
}