Reinforcement learning of humanoid rhythmic walking parameters based on visual information

Authors: Masaki Ogino; Yutaka Katoh; Masahiro Aono; Minoru Asada; Koh Hosoda

Source: Advanced Robotics, Volume 18, Number 7, 2004 , pp. 677-697(21)

Publisher: VSP, an imprint of Brill

Key:

- Free Content

- New Content

- Subscribed Content

- Free Trial Content

Abstract:

This paper presents a method for learning the parameters of rhythmic walking to generate purposive humanoid motions. The controller consists of the two layers: rhythmic walking is realized by the lower layer, which adjusts the speed of the phase on the desired trajectory depending on sensory information, and the upper layer learns (i) the feasible parameter sets that enable stable walking, (ii) the causal relationship between the walking parameters to be given to the lower-layer controller and the change in the sensory information and (iii) the feasible rhythmic walking parameters by reinforcement learning so that a robot can reach the goal based on visual information. The experimental results show that a real humanoid learns to reach the ball and to shoot it into the goal in the context of the RoboCup soccer competition, and the further issues are discussed.

Keywords: HUMANOID; REINFORCEMENT LEARNING; RHYTHMIC WALKING; VISION; ROBOT SOCCER

Document Type: Research article

DOI: 10.1163/1568553041719519

The full text electronic article is available for purchase. You will be able to download the full text electronic article after payment.

$25.00 plus tax