Learning Nonlinear Multi-Variate Motion Dynamics for Real- Time Position and Orientation Control of Robotic Manipulators

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 multivariate 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:
Proceedings of 9th IEEE-RAS International Conference on Humanoid Robots
Presented at:
9th IEEE-RAS International Conference on Humanoid Robots

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 Record created 2009-09-14, last modified 2020-07-30

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