  This paper focuses on learning a model of system dynamics online while
satisfying safety constraints.Our motivation is to avoid offline system
identification or hand-specified dynamics models and allowa system to safely
and autonomously estimate and adapt its own model during online operation.Given
streaming observations of the system state, we use Bayesian learning to obtain
a distributionover the system dynamics. In turn, the distribution is used to
optimize the system behavior andensure safety with high probability, by
specifying a chance constraint over a control barrier function.
