Cognitive Architecture for Adaptive Skill Learning towards Fluent Human-Robot Collaborative Assembly

This paper presents a new cognitive architecture to enable human-robot collaborative assembly in complex, unstructured environments. While existing human-robot collaboration technologies have demonstrated success in simple setups, they struggle with fluent interaction in complex scenarios characterized by unpredictable human intentions and flexible workspace configurations. Our approach addresses these limitations by developing a cognitive architecture built upon the SOAR architecture, emphasizing internal cognitive processes, including real-time learning, adaptive decision-making, and knowledge evolution. The proposed system integrates perception, learning, memory, and execution components into a unified architecture that enables robots to continuously acquire skills through human interaction. Through HRC experiments for assembling the Generic Assembly Box (GAB), we demonstrated a 6.74% improvement in task success rates, a 10.9% reduction in execution time, and a 15.6% decrease in human instruction needs over traditional methods. These results validate the system’s potential to bridge the gap between traditional cognitive architectures and practical robotic applications, contributing to more adaptive and intelligent human-robot collaboration.