  In this paper, we solve the problem of finding a certified control policy
that drives a robot from any given initial state and under any bounded
disturbance to the desired reference trajectory, with guarantees on the
convergence or bounds on the tracking error. Such a controller is crucial in
safe motion planning. We leverage the advanced theory in Control Contraction
Metric and design a learning framework based on neural networks to
co-synthesize the contraction metric and the controller for control-affine
systems. We further provide methods to validate the convergence and bounded
error guarantees. We demonstrate the performance of our method using a suite of
challenging robotic models, including models with learned dynamics as neural
networks. We compare our approach with leading methods using sum-of-squares
programming, reinforcement learning, and model predictive control. Results show
that our methods indeed can handle a broader class of systems with less
tracking error and faster execution speed. Code is available at
https://github.com/sundw2014/C3M.
