Predicting Rigid Body Dynamics using Dual Quaternion Recurrent Neural Networks with Quaternion Attention
This work addresses the problem of accurately predicting rigid body dynamics for applications like parcel movement simulation, offering an incremental improvement in representation and interaction modeling.
This paper proposes a novel neural network architecture using dual quaternions to represent rigid body movements and recurrent architectures to capture dynamic behavior. It also introduces a dual quaternion attention mechanism to model interactions between rigid bodies and external inputs, applying the approach to a parcel prediction problem.
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.