  Robotic manipulation can greatly benefit from the data efficiency,
robustness, and predictability of model-based methods if robots can quickly
generate models of novel objects they encounter. This is especially difficult
when effects like complex joint friction lack clear first-principles models and
are usually ignored by physics simulators. Further, numerically-stiff contact
dynamics can make common model-building approaches struggle. We propose a
method to simultaneously learn contact and continuous dynamics of a novel,
possibly multi-link object by observing its motion through contact-rich
trajectories. We formulate a system identification process with a loss that
infers unmeasured contact forces, penalizing their violation of physical
constraints and laws of motion given current model parameters. Our loss is
unlike prediction-based losses used in differentiable simulation. Using a new
dataset of real articulated object trajectories and an existing cube toss
dataset, our method outperforms differentiable simulation and end-to-end
alternatives with more data efficiency. See our project page for code,
datasets, and media: https://sites.google.com/view/continuous-contact-nets/home
