Coordinate Transformation Learning of Hand Position Feedback Controller by Using Change of Position Error Norm

Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)

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Eimei Oyama, Susumu Tachi


In order to grasp an object, we need to solve the inverse kine(cid:173) matics problem, i.e., the coordinate transformation from the visual coordinates to the joint angle vector coordinates of the arm. Al(cid:173) though several models of coordinate transformation learning have been proposed, they suffer from a number of drawbacks. In human motion control, the learning of the hand position error feedback controller in the inverse kinematics solver is important. This paper proposes a novel model of the coordinate transformation learning of the human visual feedback controller that uses the change of the joint angle vector and the corresponding change of the square of the hand position error norm. The feasibility of the proposed model is illustrated using numerical simulations.