Abstract
This paper proposes a KDD (knowledge discovery and data mining) approach to motion control for humanoid robots. The aim of this approach is to discover the knowledge for generating stable motions in balance. In this paper, we propose a motion control system, which can generate a stable and anti-tumble motion by the concept learning and the searching in the motion space: extracting some generalized motion guideposts by decision tree learner and motion generation with tracking the guidepost by hill-climbing search. The motion control system has three parts: training, learning, and generating part. Former two parts are to acquire the balancing property of itself body and movement by decision tree learning for numbers of executed training motions, and latter one part provides the search technique for the motion control based on the acquired knowledge concerning balance and stability in the motion. In this paper, some performance results by humanoid robot HOAP-1 are reported: stable and anti-tumble motions to stand up from a chair. This paper also reports some performance for the change in the environments: standing up from a chair on slope and different in height.
