Reaching Movements of a Redundant Musculoskeletal Arm: Acquisition of an Adequate Internal Force by Iterative Learning and Its Evaluation through a Dynamic Damping Ellipsoid

Abstract

This paper presents a proposal of an iterative learning control method for a musculoskeletal arm to acquire adequate internal force in order to realize human-like natural movements. Additionally, a dynamic damping ellipsoid at the end-point is introduced to evaluate internal forces obtained through the iterative learning scheme. Our previous works presented that a human-like smooth reaching movement using a musculoskeletal redundant arm model can be achieved by introducing a nonlinear muscle model and 'the virtual spring–damper hypothesis'. However, to date, the internal forces have been determined heuristically. As described in this paper, to determine internal forces more systematically, an iterative learning control method is used for acquisition of an adequate dynamic damping ellipsoid according to a given task. It is presented that the learning control method can perform effectively to realize given tasks, even though strong nonlinear characteristics of the muscles exist. After acquiring a given task, the dynamic damping ellipsoid is introduced to evaluate the relation between a damping effect generated by the acquired internal forces and a trajectory of the end-point. Some numerical simulations are performed and the usefulness of the learning control strategy, despite strong nonlinearity, is demonstrated through these results.