  This paper focuses on learning a model of system dynamics online while
satisfying safety constraints. Our objective is to avoid offline system
identification or hand-specified models and allow a system to safely and
autonomously estimate and adapt its own model during operation. Given streaming
observations of the system state, we use Bayesian learning to obtain a
distribution over the system dynamics. Specifically, we use a matrix variate
Gaussian process (MVGP) regression approach with efficient covariance
factorization to learn the drift and input gain terms of a nonlinear
control-affine system. The MVGP distribution is then used to optimize the
system behavior and ensure safety with high probability, by specifying control
Lyapunov function (CLF) and control barrier function (CBF) chance constraints.
We show that a safe control policy can be synthesized for systems with
arbitrary relative degree and probabilistic CLF-CBF constraints by solving a
second order cone program (SOCP). Finally, we extend our design to a
self-triggering formulation, adaptively determining the time at which a new
control input needs to be applied in order to guarantee safety.
