Real-time adaptation technique to real robots: an experiment with a humanoid robot

We introduce a technique that allows a real robot to execute a real-time learning, in which GP and RL are integrated. In our former research, we showed the result of an experiment with a real robot "AIBO" and proved the technique performed better than the traditional Q-learning method. Based on the proposed technique, we can acquire the common programs using a GP, applicable to various types of robots. We execute reinforcement learning with the acquired program in a real robot. In this way, the robot can adapt to its own operational characteristics and learn effective actions. In this paper, we show the experimental results in which a humanoid robot "HOAP-1" has been evolved to perform effectively to solve the box-moving task.