Adapting Multi-Robot Behavior to Communication Atmosphere in Humans-Robots Interaction Using Fuzzy Production Rule Based Friend-Q Learning

A behavior adaptation mechanism in humans-robots interaction is proposed to adjust robots’ behavior to communication atmosphere, where fuzzy production rule based friend-Q learning (FPRFQ) is introduced. It aims to shorten the response time of robots and decrease the social distance between humans and robots to realize the smooth communication of robots and humans. Experiments on robots/humans interaction are performed in a virtual communication atmosphere environment. Results show that robots adapt well by saving 44 and 482 learning steps compared to that by friend-Q learning (FQ) and independent learning (IL), respectively; additionally, the distance between human-generated atmosphere and robot-generated atmosphere is 3 times and 10 times shorter than the FQ and the IL, respectively. The proposed behavior adaptation mechanism is also applied to robots’ eye movement in the developing humans-robots interaction system, calledmascot robot system, and basic experimental results are shown in home party scenario with five eye robots and four humans.