  Machines that can predict the effect of physical interactions on the dynamics
of previously unseen object instances are important for creating better robots
and interactive virtual worlds. In this work, we focus on predicting the
dynamics of 3D objects on a plane that have just been subjected to an impulsive
force. In particular, we predict the changes in state - 3D position, rotation,
velocities, and stability. Different from previous work, our approach can
generalize dynamics predictions to object shapes and initial conditions that
were unseen during training. Our method takes the 3D object's shape as a point
cloud and its initial linear and angular velocities as input. We extract shape
features and use a recurrent neural network to predict the full change in state
at each time step. Our model can support training with data from both a physics
engine or the real world. Experiments show that we can accurately predict the
changes in state for unseen object geometries and initial conditions.
