TR2023-132

Physics Informed Gaussian Process Regression Methods for Robot Inverse Dynamics Identification


    •  Giacomuzzo, G., Dalla Libera, A., Romeres, D., Carli, R., "Physics Informed Gaussian Process Regression Methods for Robot Inverse Dynamics Identification", Conferenza Italiana di Robotica e Macchine Intelligenti, October 2023.
      BibTeX TR2023-132 PDF
      • @inproceedings{Giacomuzzo2023oct2,
      • author = {Giacomuzzo, Giulio and Dalla Libera, Alberto and Romeres, Diego and Carli, Ruggero},
      • title = {Physics Informed Gaussian Process Regression Methods for Robot Inverse Dynamics Identification},
      • booktitle = {Conferenza Italiana di Robotica e Macchine Intelligenti},
      • year = 2023,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2023-132}
      • }
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  • Research Area:

    Robotics

Abstract:

In this extended abstract we present two recent contributions in the context of Physics Informed black-box inverse dynamics identification using Gaussian Processes (GPs). The first contribution consists in a novel kernel, named Geometrically inspired Polynomial Kernel (GIP) for single joint GP-based inverse dynamics identification. Driven by the fact that the inverse dynamics can be described as a polynomial function on a suitable input space, the GIP kernel restricts the regression problem to a finite-dimensional space which contains the inverse dynamics function, thus leading to improved data efficiency and generalization properties. The second contribution consists in the derivation of a multidimensional GP framework, named Lagrangian GPR, which overcomes the single joint approach and learns the inverse dynamics in a multidimensional setting. Exploiting the properties of GPs in connection with linear operators, Lagrangian GPR allows to impose by design the known symmetric structure of the Euler-Lagrange equation on the learned models. Moreover, since information is shared between different degrees of freedom (DOFs), this approach strongly improves data efficiency and generalization properties.

 

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