Software & Data Downloads — LIP4RobotInverseDynamics

Lagrangian Inspired Polynomial for Robot Inverse Dynamics for the identification of the inverse dynamics of robotic manipulators.

Learning the inverse dynamics of robots directly from data, adopting a black-box approach, is interesting for several real-world scenarios where limited knowledge about the system is available. This code repository proposes a black-box model, based on Gaussian Process (GP) Regression for the identification of the inverse dynamics of robotic manipulators. The proposed model relies on a novel multidimensional kernel, called Lagrangian Inspired Polynomial (LIP) kernel. The LIP kernel is based on two main ideas. First, instead of directly modeling the inverse dynamics components, we model as GPs the kinetic and potential energy of the system. The GP prior on the inverse dynamics components is derived from those on the energies by applying the properties of GPs under linear operators. Second, as regards the energy prior definition, we prove a polynomial structure of the kinetic and potential energy, and we derive a polynomial kernel that encodes this property. As a consequence, the proposed model allows also to estimate the kinetic and potential energy without requiring any label on these quantities. The software was tested in simulation and on two real robotic manipulators, namely a 7 DOF Franka Emika Panda, and a 6 DOF MELFA RV4FL. Please refer to the paper "A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification" for more explanation.

    •  Giacomuzzo, G., Dalla Libera, A., Romeres, D., Carli, R., "A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification", IEEE Transaction on Robotics, August 2024.
      BibTeX TR2024-077 PDF Data Software
      • @article{Giacomuzzo2024aug2,
      • author = {{Giacomuzzo, Giulio and Dalla Libera, Alberto and Romeres, Diego and Carli, Ruggero}},
      • title = {A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification},
      • journal = {IEEE Transaction on Robotics},
      • year = 2024,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2024-077}
      • }

    Access software at https://github.com/merlresearch/LIP4RobotInverseDynamics.

    Access data at https://doi.org/10.5281/zenodo.12516500.