Control of multi-legged robot using reinforcement learning with body image and application to rubble Online publication date: Sat, 12-Jul-2014
by Kazuya Nishigai; Hiroki Nakatsuka; Kazuyuki Ito
International Journal of Advanced Mechatronic Systems (IJAMECHS), Vol. 5, No. 4, 2013
Abstract: In this paper, we address the autonomous control of a 6-legged robot using reinforcement learning, and apply it to rubbles. In conventional framework of reinforcement learning, many degrees of freedom of the 6-legged robot cause the state explosion problem, and it prevents the robot from real-time learning. Furthermore, even if learning process is conducted in simulation, the acquired policy is effective only for the simple simulated environment, and therefore, it is impossible to apply the acquired policy to unknown complex environment such as rubbles. To solve these problems, we focus on the flexibility of the body and its body image. The body image solves the state explosion problem and the flexibility of the body compensates the difference between the simple simulated environment and the complex real environment. We developed a prototype of the robot and conducted experiments to confirm the effectiveness and the validity of the proposed framework. As a result, effective locomotion was realised.
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