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Learning of robot arm impedance in operational space using neural networks

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3 Author(s)
Tsuji, T. ; Fac. of Eng., Hiroshima Univ., Japan ; Ito, K. ; Morasso, Pietro

Impedance control is one of the most effective control methods for the manipulators in contact with their environments. The characteristic of force and motion control, however, is influenced by a desired impedance of a manipulator's end-effector, which must be designed according to a given task and an environment. The present paper proposes a new method to regulate the impedance of the end-effector through learning of neural networks. The method can regulate not only stiffness and viscosity but also the inertia and virtual trajectory of the end-effector and can realize a smooth transition from free to contact movements by regulating the impedance parameters before a contact.

Published in:

Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on  (Volume:1 )

Date of Conference:

25-29 Oct. 1993

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