  Common methods for learning robot dynamics assume motion is continuous,
causing unrealistic model predictions for systems undergoing discontinuous
impact and stiction behavior. In this work, we resolve this conflict with a
smooth, implicit encoding of the structure inherent to contact-induced
discontinuities. Our method, ContactNets, learns parameterizations of
inter-body signed distance and contact-frame Jacobians, a representation that
is compatible with many simulation, control, and planning environments for
robotics. We furthermore circumvent the need to differentiate through stiff or
non-smooth dynamics with a novel loss function inspired by the principles of
complementarity and maximum dissipation. Our method can predict realistic
impact, non-penetration, and stiction when trained on 60 seconds of real-world
data.
