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Learning nonlinear multi-variate motion dynamics for real-time position and orientation control of robotic manipulators

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2 Author(s)
Gribovskaya, E. ; Learning Algorithms & Syst. Lab. (LASA), Ecole Polythechnique Fed. de Lausanne, Lausanne, Switzerland ; Billard, A.

We present a generic framework that allows learning non-linear dynamics of motion in manipulation tasks and generating dynamical laws for control of position and orientation. This work follows a recent trend in Programming by Demonstration in which the dynamics of an arm motion is learned: position and orientation control are learned as multi-variate dynamical systems to preserve correlation within the signals. The strength of the method is three-fold: (i) it extracts dynamical control laws from demonstrations, and subsequently provides concurrent smooth control of both position and orientation; (ii) it allows to generalize a motion to unseen context; (iii) it guarantees on-line adaptation of the motion in the face of spatial and temporal perturbations. The method is validated to control a four degree of freedom humanoid arm and an industrial six degree of freedom robotic arm.

Published in:

Humanoid Robots, 2009. Humanoids 2009. 9th IEEE-RAS International Conference on

Date of Conference:

7-10 Dec. 2009