Mutual Reinforcement Learning with Robot Trainers

Abstract

The researchers in this study have developed a novel approach using mutual reinforcement learning (MRL) where both the robot and human act as empathetic individuals who function as reinforcement learning agents for each other to achieve a particular task over continuous communication and feedback. This shared model not only has a collective impact but improves human cognition and helps in building a successful human-robot relationship. In our current work, we compared our learned reinforcement model with a baseline non-reinforcement and random approach in a robotics domain to identify the significance and impact of MRL. MRL contributed to improved skill transfer, and the robot was able successfully to predict which reinforcement behaviors would be most valuable to its human partners.