An Interactive Multisensing Framework for Personalized Human Robot Collaboration and Assistive Training Using Reinforcement Learning

摘要

There is a recent trend of research and applications of Cyber-Physical Systems (CPS) in manufacturing to enhance human-robot collaboration and production. In this paper, we propose a CPS framework for personalized Human-Robot Collaboration and Training to promote safe human-robot collaboration in manufacturing environments. We propose a human-centric CPS approach that focuses on multimodal human behavior monitoring and assessment, to promote human worker safety and enable human training in Human-Robot Collaboration tasks. We present the architecture of our proposed system, our experimental testbed and our proposed methods for multimodal physiological sensing, human state monitoring and interactive robot adaptation, to enable personalized interaction.