Robust Policy Learning for Human-Robot Collaboration in Unstructured Industrial Environments

This paper introduces a novel robust policy learning framework enabling seamless and safe human-robot collaboration in complex, unstructured industrial settings. The approach leverages advanced perception and control algorithms, allowing robots to adapt to dynamic human actions and environmental changes. This significantly enhances operational safety, efficiency, and flexibility in manufacturing and logistics, paving the way for more intuitive and productive human-robot teams.