Learning whole upper body control with dynamic redundancy resolution in coupled associative radial basis function networks

We present a dynamical system approach to learning forward and inverse kinematics of a humanoid upper body in associative radial basis function networks. Coupling of arm kinematics via the torso joints is modeled by dynamically coupling two networks learning the direct inverse kinematics of both torso-arm chains separately. Dividing the upper body kinematics in two problems significantly reduces the number of samples required for learning. Redundancies of the inverse kinematics are represented by multi-stable dynamics of the associative networks and are resolved dynamically depending on the current system state. The model is exploited for task space tracking in a feedback control framework.