  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.
