Learning Predictive Visuomotor Coordination

Abstract:Understanding and predicting human visuomotor coordination is crucial for applications in robotics, human-computer interaction, and assistive technologies. This work introduces a forecasting-based task for visuomotor modeling, where the goal is to predict head pose, gaze, and upper-body motion from egocentric visual and kinematic observations. We propose a \textit{Visuomotor Coordination Representation} (VCR) that learns structured temporal dependencies across these multimodal signals. We extend a diffusion-based motion modeling framework that integrates egocentric vision and kinematic sequences, enabling temporally coherent and accurate visuomotor predictions. Our approach is evaluated on the large-scale EgoExo4D dataset, demonstrating strong generalization across diverse real-world activities. Our results highlight the importance of multimodal integration in understanding visuomotor coordination, contributing to research in visuomotor learning and human behavior modeling. Project Page: this https URL.

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

arXiv:2503.23300 [cs.CV]

(or arXiv:2503.23300v2 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2503.23300 Focus to learn more arXiv-issued DOI via DataCite

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