Using Physics Knowledge for Learning Rigid-body Forward Dynamics with Gaussian Process Force PriorsDownload PDF

Published: 13 Sept 2021, Last Modified: 05 May 2023CoRL2021 PosterReaders: Everyone
Keywords: Machine Learning, Robotics, Analytical mechanics
Abstract: If a robot's dynamics are difficult to model solely through analytical mechanics, it is an attractive option to directly learn it from data. Yet, solely data-driven approaches require considerable amounts of data for training and do not extrapolate well to unseen regions of the system's state space. In this work, we emphasize that when a robot's links are sufficiently rigid, many analytical functions such as kinematics, inertia functions, and surface constraints encode informative prior knowledge on its dynamics. To this effect, we propose a framework for learning probabilistic forward dynamics that combines physics knowledge with Gaussian processes utilizing automatic differentiation with GPU acceleration. Compared to solely data-driven modeling, the model's data efficiency improves while the model also respects physical constraints. We illustrate the proposed structured model on a seven joint robot arm in PyBullet. Our implementation of the proposed framework can be found here:
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