  Ensuring the safe operation of legged robots in uncertain, novel environments
is crucial to their widespread adoption. Despite recent advances in safety
filters that can keep arbitrary task-driven policies from incurring safety
failures, existing solutions for legged robot locomotion still rely on
simplified dynamics and may fail when the robot is perturbed away from
predefined stable gaits. This paper presents a general approach that leverages
offline game-theoretic reinforcement learning to synthesize a highly robust
safety filter for high-order nonlinear dynamics. This gameplay filter then
maintains runtime safety by continually simulating adversarial futures and
precluding task-driven actions that would cause it to lose future games (and
thereby violate safety). Validated on a 36-dimensional quadruped robot
locomotion task, the gameplay safety filter exhibits inherent robustness to the
sim-to-real gap without manual tuning or heuristic designs. Physical
experiments demonstrate the effectiveness of the gameplay safety filter under
perturbations, such as tugging and unmodeled irregular terrains, while
simulation studies shed light on how to trade off computation and
conservativeness without compromising safety.
