  The deployment of robots in uncontrolled environments requires them to
operate robustly under previously unseen scenarios, like irregular terrain and
wind conditions. Unfortunately, while rigorous safety frameworks from robust
optimal control theory scale poorly to high-dimensional nonlinear dynamics,
control policies computed by more tractable "deep" methods lack guarantees and
tend to exhibit little robustness to uncertain operating conditions. This work
introduces a novel approach enabling scalable synthesis of robust
safety-preserving controllers for robotic systems with general nonlinear
dynamics subject to bounded modeling error by combining game-theoretic safety
analysis with adversarial reinforcement learning in simulation. Following a
soft actor-critic scheme, a safety-seeking fallback policy is co-trained with
an adversarial "disturbance" agent that aims to invoke the worst-case
realization of model error and training-to-deployment discrepancy allowed by
the designer's uncertainty. While the learned control policy does not
intrinsically guarantee safety, it is used to construct a real-time safety
filter (or shield) with robust safety guarantees based on forward reachability
rollouts. This shield can be used in conjunction with a safety-agnostic control
policy, precluding any task-driven actions that could result in loss of safety.
We evaluate our learning-based safety approach in a 5D race car simulator,
compare the learned safety policy to the numerically obtained optimal solution,
and empirically validate the robust safety guarantee of our proposed safety
shield against worst-case model discrepancy.
