Predicting Rigid Body Dynamics using Dual Quaternion Recurrent Neural Networks with Quaternion Attention

TL;DR

This paper introduces a novel neural network architecture utilizing dual quaternions and quaternion attention to efficiently model and predict rigid body dynamics, capturing complex interactions in a compact and trainable framework.

Contribution

The paper presents a new dual quaternion-based recurrent neural network with quaternion attention for improved rigid body motion prediction.

Findings

Accurately predicts rigid body movements in simulation environments.

Efficiently models interactions between multiple rigid bodies and external inputs.

Demonstrates the effectiveness of dual quaternion representations in neural networks.

Abstract

We propose a novel neural network architecture based on dual quaternions which allow for a compact representation of informations with a main focus on describing rigid body movements. To cover the dynamic behavior inherent to rigid body movements, we propose recurrent architectures in the neural network. To further model the interactions between individual rigid bodies as well as external inputs efficiently, we incorporate a novel attention mechanism employing dual quaternion algebra. The introduced architecture is trainable by means of gradient based algorithms. We apply our approach to a parcel prediction problem where a rigid body with an initial position, orientation, velocity and angular velocity moves through a fixed simulation environment which exhibits rich interactions between the parcel and the boundaries.