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Movement imitation with nonlinear dynamical systems in humanoid robots

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3 Author(s)
Ijspeert, A.J. ; Computational Learning & Motor Control Lab., Univ. of Southern California, Los Angeles, CA, USA ; Nakanishi, J. ; Schaal, S.

Presents an approach to movement planning, on-line trajectory modification, and imitation learning by representing movement plans based on a set of nonlinear differential equations with well-defined attractor dynamics. The resultant movement plan remains an autonomous set of nonlinear differential equations that forms a control policy (CP) which is robust to strong external perturbations and that can be modified on-line by additional perceptual variables. We evaluate the system with a humanoid robot simulation and an actual humanoid robot. Experiments are presented for the imitation of three types of movements: reaching movements with one arm, drawing movements of 2-D patterns, and tennis swings. Our results demonstrate (a) that multi-joint human movements can be encoded successfully by the CPs, (b) that a learned movement policy can readily be reused to produce robust trajectories towards different targets, (c) that a policy fitted for one particular target provides a good predictor of human reaching movements towards neighboring targets, and (d) that the parameter space which encodes a policy is suitable for measuring to which extent two trajectories are qualitatively similar

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Robotics and Automation, 2002. Proceedings. ICRA '02. IEEE International Conference on  (Volume:2 )

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