Real-Time Learning of Predictive Dynamic Obstacle Models for Robotic Motion Planning

arXiv — cs.LG•Tuesday, November 4, 2025 at 5:00:00 AM

A recent study explores the potential of real-time learning in robotics, specifically focusing on how autonomous systems can predict the movements of nearby agents using a novel approach called Hankel Dynamic Mode Decomposition. This method enhances the ability of robots to operate in dynamic environments by improving their predictive capabilities, which is crucial for safe and efficient navigation. As robotics technology continues to advance, such innovations could significantly impact various industries, making robots more adaptable and intelligent.

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