Learning from Human Collaborative Experience
Jeffrey Too Chuan Tan, Yoshinobu Hagiwara, Tetsunari Inamura · 2017
Human-robot collaboration is a potential yet challenging robot development due to the vase diversity of partner human behaviors for the robot to adapt. In this work, we develop a robot learning framework that can learn by data-driven approach, where collaboration data is collected through crowdsourcing of human-robot interaction. We propose the addition of a formal definition incorporating partner's behaviors and set of state features for work conditions related to the collaboration task into the learning policy. A collaborative table setting task experiment scenario was developed with the capability to perform cloud based human-robot interaction for crowdsourcing data gathering. The human-human collaboration experiments were conducted to gather collaboration interaction data to build the case based planning libraries and finally, evaluation experiments were conducted and concluded the effectiveness of the proposed learning approach.