  The ability to learn and execute optimal control policies safely is critical
to realization of complex autonomy, especially where task restarts are not
available and/or the systems are safety-critical. Safety requirements are often
expressed in terms of state and/or control constraints. Methods such as barrier
transformation and control barrier functions have been successfully used, in
conjunction with model-based reinforcement learning, for safe learning in
systems under state constraints, to learn the optimal control policy. However,
existing barrier-based safe learning methods rely on full state feedback. In
this paper, an output-feedback safe model-based reinforcement learning
technique is developed that utilizes a novel dynamic state estimator to
implement simultaneous learning and control for a class of safety-critical
systems with partially observable state.
