Human-Guided Transfer Learning for Autonomous Robot

Minako Oriyama, Pitoyo Hartono, Hideyuki Sawada

Abstract: In recent years, neural networks have been successfully applied to many problems. However, prohibitively long learning time and vast training data are sometimes unavoidable. While the long learning time can be tolerated for many problems, it is crucial for autonomous robots learning in physical environments. One way to alleviate this problem is through transfer learning, which applies knowledge from one domain to another. In this study, we propose a method for transferring human common sense for guiding the subsequent reinforcement learning of a robot for real-time learning in a more complex environment. The efficacy of the transfer mechanism is analyzed to obtain new insights on the required prior knowledge to be transferred for training an autonomous robot. Different types of prior knowledge were analyzed and explained in this paper.