Reinforcement learning of periodical gaits in locomotion robots

Mikhail Svinin; Kazuyaki Yamada; S. Ushio; Kanji Ueda

doi:10.1117/12.360338

26 August 1999 Reinforcement learning of periodical gaits in locomotion robots

Mikhail Svinin, Kazuyaki Yamada, S. Ushio, Kanji Ueda

Proceedings Volume 3839, Sensor Fusion and Decentralized Control in Robotic Systems II; (1999) https://doi.org/10.1117/12.360338
Event: Photonics East '99, 1999, Boston, MA, United States

Abstract

Emergence of stable gaits in locomotion robots is studied in this paper. A classifier system, implementing an instance- based reinforcement learning scheme, is used for sensory- motor control of an eight-legged mobile robot. Important feature of the classifier system is its ability to work with the continuous sensor space. The robot does not have a prior knowledge of the environment, its own internal model, and the goal coordinates. It is only assumed that the robot can acquire stable gaits by learning how to reach a light source. During the learning process the control system, is self-organized by reinforcement signals. Reaching the light source defines a global reward. Forward motion gets a local reward, while stepping back and falling down get a local punishment. Feasibility of the proposed self-organized system is tested under simulation and experiment. The control actions are specified at the leg level. It is shown that, as learning progresses, the number of the action rules in the classifier systems is stabilized to a certain level, corresponding to the acquired gait patterns.

Citation Download Citation

Mikhail Svinin, Kazuyaki Yamada, S. Ushio, and Kanji Ueda "Reinforcement learning of periodical gaits in locomotion robots", Proc. SPIE 3839, Sensor Fusion and Decentralized Control in Robotic Systems II, (26 August 1999); https://doi.org/10.1117/12.360338

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