  Frictional contact has been extensively studied as the core underlying
behavior of legged locomotion and manipulation, and its nearly-discontinuous
nature makes planning and control difficult even when an accurate model of the
robot is available. Here, we present empirical evidence that learning an
accurate model in the first place can be confounded by contact, as modern deep
learning approaches are not designed to capture this non-smoothness. We isolate
the effects of contact's non-smoothness by varying the mechanical stiffness of
a compliant contact simulator. Even for a simple system, we find that stiffness
alone dramatically degrades training processes, generalization, and
data-efficiency. Our results raise serious questions about simulated testing
environments which do not accurately reflect the stiffness of rigid robotic
hardware. Significant additional investigation will be necessary to fully
understand and mitigate these effects, and we suggest several avenues for
future study.

Robotics