Paper

Learning Predictive Visuomotor Coordination

arXiv:2503.23300v2 Announce Type: replace 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 th…

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