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Neural learning and dynamical selection of redundant solutions for inverse kinematic control

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2 Author(s)
Reinhart, R.F. ; Res. Inst. for Cognition & Robot. (CoR-Lab.), Bielefeld Univ., Bielefeld, Germany ; Steil, J.J.

We introduce a novel recurrent neural network controller that learns and maintains multiple solutions of the inverse kinematics. Redundancies are resolved dynamically by means of multi-stable attractor dynamics. The associative net- work comprises a combined forward and inverse model of the robot's kinematics and enables flexible selection of control spaces by mixing constraints in task space and joint space. The network is integrated into a feedforward-feedback control framework which enables dynamical movement generation. We show results for the humanoid robot iCub in simulation.

Published in:

Humanoid Robots (Humanoids), 2011 11th IEEE-RAS International Conference on

Date of Conference:

26-28 Oct. 2011

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