A simple rule how to make a reward for learning with human interaction

摘要

Various learning methods are adapted for experimental robot. We can make movement of a robot by giving teaching signals to a robot. But it is heavy for operator to define how to give teaching signals generally because operator must guess and think of a task and environment and define a function to do that. Here the author aim to create teaching signals automatically for each task and environment. In this paper, the author suggest a simple rule which is independent of information about any task and environment to create teaching signals for each task and environment. This rule is that a situation which is often happened is good situation. In this paper, the author adopt reinforcement learning as learning method and a small-sized humanoid robot as application. The author show creating a reward by adapting a rule and show that a robot can learn and make movement.