Embodiment Mapping Method between Heterogeneous Robots based on Self-body Representation for Transfer Learning in Reinforcement Learning

Abstract

This paper presents a novel transfer learning method in reinforcement learning. The method is inspired cognitive psychological theory which is self-body representation. In recent years, intelligent robot technology such as reinforcement learning has been discussed for the real world application. As a recent technique, transfer learning is proposed for reducing the learning time of reinforcement learning by reusing obtained policies. However, transfer learning is needed to be designed mapping features between body structure of robots. The mapping called inter-task mapping in transfer reinforcement learning. For example, correspondent motor and motion of the robot, and it is designed by human. In this paper, mapping method is proposed inspiring self-body representation which is suggested in cognitive psychology, further evaluate the effectiveness of proposed method in experiment with small size humanoid robot.