Learning the Inverse Dynamics of Robotic Manipulators in Structured Reproducing Kernel Hilbert Space

IEEE Transactions on Cybernetics
Ching-An ChengChih-Chun Cheng

Abstract

We investigate the modeling of inverse dynamics without prior kinematic information for holonomic rigid-body robots. Despite success in compensating robot dynamics and friction, general inverse dynamics models are nontrivial. Rigid-body models are restrictive or inefficient; learning-based models are generalizable yet require large training data. The structured kernels address the dilemma by embedding the robot dynamics in reproducing kernel Hilbert space. The proposed kernels autonomously converge to rigid-body models but require fewer samples; with a semi-parametric framework that incorporates additional parametric basis for friction, the structured kernels can efficiently model general rigid-body robots. We tested the proposed scheme in simulations and experiments; the models that consider the structure of function space are more accurate.

References

Sep 17, 2004·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Dragan Kukolj, Emil Levi
Feb 5, 2008·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·A YazdizadehR V Patel
Jul 1, 2012·IEEE Transactions on Neural Networks and Learning Systems·Hassan A KingraviEric N Johnson
Feb 27, 2015·IEEE Transactions on Neural Networks and Learning Systems·Girish ChowdharyPatricio A Vela

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Citations

Dec 17, 2015·IEEE Transactions on Cybernetics·Ching-An Cheng, Han-Pang Huang

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