Motion control for humanoid robots based on the concept learning

Abstract

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.