Adaptive Body Schema Learning Considering Muscle Addition for Musculoskeletal Humanoids

Abstract

One of the important advantages of musculoskeletal humanoids is that it is easy to change the muscle arrangement and to increase the number of muscles according to the situation. In this study, we describe an overall system of adaptive body schema learning that can relearn the changes of body schema with muscle addition of musculoskeletal humanoids from a small amount of motion data. We apply our method to a simple 1-DOF tendon-driven robot simulation and the arm of the musculoskeletal humanoid Musashi, and show the effectiveness of muscle tension relaxation by adding muscles for a high-load task.