Before revision:  In this work, we develop a framework for probabilistically safe operation with uncertain dynamics . This framework relies on Bayesian meta-learning for efficient inference of system dynamics with calibrated uncertainty.
After revision:  In this work, we propose a practical and theoretically justified approach to maintaining safety in the presence of dynamics uncertainty. Our approach leverages Bayesian meta-learning for efficient inference of system dynamics with calibrated uncertainty.