Spatio-Temporal Contrastive Learning for Human Action Anticipation in Autonomous Robotic Systems

**Abstract:** This paper proposes a novel framework for human action anticipation in autonomous robotic systems leveraging spatio-temporal contrastive learning. Existing action prediction models often struggle with accurately forecasting multi-step future actions due to temporal drift and occlusion. Our method, ST-CoLA (Spatio-Temporal Contrastive Learning for Anticipation), addresses these limitations by enforcing consistency between representations of video segments at different temporal granularities and spatial viewpoints. We demonstrate a significant improvement in action anticipation accuracy across various datasets, facilitating more robust and adaptive autonomous robotic behavior. This framework has direct implications for robotic navigation in shared human environments, collaborative task execution, and personalized assistive robotics, offering a multi-billion dollar market opportunity. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*