  A limitation of model-based reinforcement learning (MBRL) is the exploitation
of errors in the learned models. Black-box models can fit complex dynamics with
high fidelity, but their behavior is undefined outside of the data
distribution.Physics-based models are better at extrapolating, due to the
general validity of their informed structure, but underfit in the real world
due to the presence of unmodeled phenomena. In this work, we demonstrate
experimentally that for the offline model-based reinforcement learning setting,
physics-based models can be beneficial compared to high-capacity function
approximators if the mechanical structure is known. Physics-based models can
learn to perform the ball in a cup (BiC) task on a physical manipulator using
only 4 minutes of sampled data using offline MBRL. We find that black-box
models consistently produce unviable policies for BiC as all predicted
trajectories diverge to physically impossible state, despite having access to
more data than the physics-based model. In addition, we generalize the approach
of physics parameter identification from modeling holonomic multi-body systems
to systems with nonholonomic dynamics using end-to-end automatic
differentiation.
  Videos: https://sites.google.com/view/ball-in-a-cup-in-4-minutes/
