Learning Interpretable Dynamics from Images of a Freely Rotating 3D Rigid Body

• URL: http://arxiv.org/abs/2209.11355v3
• Date: Wed, 23 Aug 2023 14:51:47 GMT
• Title: Learning Interpretable Dynamics from Images of a Freely Rotating 3D Rigid Body
• Authors: Justice Mason and Christine Allen-Blanchette and Nicholas Zolman and Elizabeth Davison and Naomi Leonard
• Abstract summary: We present a physics-informed neural network model to estimate and predict 3D rotational dynamics from image sequences. We demonstrate the efficacy of our approach on a new rotating rigid-body dataset with sequences of rotating cubes and rectangular prisms with uniform and non-uniform density.
• Score: 1.143707646428782
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In many real-world settings, image observations of freely rotating 3D rigid bodies, such as satellites, may be available when low-dimensional measurements are not. However, the high-dimensionality of image data precludes the use of classical estimation techniques to learn the dynamics and a lack of interpretability reduces the usefulness of standard deep learning methods. In this work, we present a physics-informed neural network model to estimate and predict 3D rotational dynamics from image sequences. We achieve this using a multi-stage prediction pipeline that maps individual images to a latent representation homeomorphic to $\mathbf{SO}(3)$, computes angular velocities from latent pairs, and predicts future latent states using the Hamiltonian equations of motion with a learned representation of the Hamiltonian. We demonstrate the efficacy of our approach on a new rotating rigid-body dataset with sequences of rotating cubes and rectangular prisms with uniform and non-uniform density.

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