  In this paper, we study the problem of ensuring safety with a few shots of
samples for partially unknown systems. We first characterize a fundamental
limit when producing safe actions is not possible due to insufficient
information or samples. Then, we develop a technique that can generate provably
safe actions and recovery behaviors using a minimum number of samples. In the
performance analysis, we also establish Nagumos theorem - like results with
relaxed assumptions, which is potentially useful in other contexts. Finally, we
discuss how the proposed method can be integrated into a policy gradient
algorithm to assure safety and stability with a handful of samples without
stabilizing initial policies or generative models to probe safe actions.
