  Reinforcement learning (RL) is capable of sophisticated motion planning and
control for robots in uncertain environments. However, state-of-the-art deep RL
approaches typically lack safety guarantees, especially when the robot and
environment models are unknown. To justify widespread deployment, robots must
respect safety constraints without sacrificing performance. Thus, we propose a
Black-box Reachability-based Safety Layer (BRSL) with three main components:
(1) data-driven reachability analysis for a black-box robot model, (2) a
trajectory rollout planner that predicts future actions and observations using
an ensemble of neural networks trained online, and (3) a differentiable
polytope collision check between the reachable set and obstacles that enables
correcting unsafe actions. In simulation, BRSL outperforms other
state-of-the-art safe RL methods on a Turtlebot 3, a quadrotor, a
trajectory-tracking point mass, and a hexarotor in wind with an unsafe set
adjacent to the area of highest reward.
