  An outstanding challenge for the widespread deployment of robotic systems
like autonomous vehicles is ensuring safe interaction with humans without
sacrificing performance. Existing safety methods often neglect the robot's
ability to learn and adapt at runtime, leading to overly conservative behavior.
This paper proposes a new closed-loop paradigm for synthesizing safe control
policies that explicitly account for the robot's evolving uncertainty and its
ability to quickly respond to future scenarios as they arise, by jointly
considering the physical dynamics and the robot's learning algorithm. We
leverage adversarial reinforcement learning for tractable safety analysis under
high-dimensional learning dynamics and demonstrate our framework's ability to
work with both Bayesian belief propagation and implicit learning through large
pre-trained neural trajectory predictors.
