Predicting 3D Rigid Body Dynamics with Deep Residual Network

TL;DR

This paper demonstrates that deep residual networks can effectively predict complex 3D rigid body dynamics, outperforming baseline methods in accuracy and capturing intricate physical interactions.

Contribution

The study introduces a novel framework combining a 3D physics simulator with a deep residual network for accurate prediction of 3D rigid body dynamics.

Findings

Achieved 25% improvement over baseline in prediction accuracy.

Successfully modeled elastic collisions and rotational dynamics.

Demonstrated potential for physics-informed machine learning in complex systems.

Abstract

This study investigates the application of deep residual networks for predicting the dynamics of interacting three-dimensional rigid bodies. We present a framework combining a 3D physics simulator implemented in C++ with a deep learning model constructed using PyTorch. The simulator generates training data encompassing linear and angular motion, elastic collisions, fluid friction, gravitational effects, and damping. Our deep residual network, consisting of an input layer, multiple residual blocks, and an output layer, is designed to handle the complexities of 3D dynamics. We evaluate the network's performance using a datasetof 10,000 simulated scenarios, each involving 3-5 interacting rigid bodies. The model achieves a mean squared error of 0.015 for position predictions and 0.022 for orientation predictions, representing a 25% improvement over baseline methods. Our results demonstrate the network's ability to capture intricate physical interactions, with particular success in predicting elastic collisions and rotational dynamics. This work significantly contributes to physics-informed machine learning by showcasing the immense potential of deep residual networks in modeling complex 3D physical systems. We discuss our approach's limitations and propose future directions for improving generalization to more diverse object shapes and materials.