Cooperative transportation by humanoid robots: learning to correct positioning

Abstract

In this paper, we describe a cooperative transportation problem with two humanoid robots and introduce a machine learning approach to solving the problem. The difficulty of the task lies on the fact that each position shifts with the other's while they are moving. Therefore, it is necessary to correct the position in a real-time manner. However, it is difficult to generate such an action in consideration of the physical formula. We empirically show how successful the humanoid robot HOAP- 1's cooperate with each other for the sake of the transportation as a result of Q-learning.