A Concept Learning Based Approach to Motion Control for Humanoid Robots

This paper proposes a concept learning-based approach to motion control for humanoid robots. In this approach, the motion control system is implemented with decision tree learner for the acquisition of balancing property of itself body and movement and depth first search technique for the motion control based on the knowledge concerning balance and stability in the motion. Some performance results by humanoid robot HOAP-1 is reported: stable and anti-tumble motions to stand up from a chair. This paper also reports some performance for the change in the environments; stand up from a chair on slope and different in height.