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Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning
[article]
2019
arXiv
pre-print
Motivated by the recursive Newton-Euler formulation, we propose a novel cascaded Gaussian process learning framework for the inverse dynamics of robot manipulators. This approach leads to a significant dimensionality reduction which in turn results in better learning and data efficiency. We explore two formulations for the cascading: the inward and outward, both along the manipulator chain topology. The learned modeling is tested in conjunction with the classical inverse dynamics model
arXiv:1910.02291v1
fatcat:idfmp74ubjfcxdx56di3ntnbre