“…Other reinforcement learning methods, including ours, learn directly from the robot's experiences [30], [31], [32], [33], [34], [35], [36], [37]. However, these methods typically assume catastrophic failures are acceptable [32], [37] or access to a safe controller [33], [34], the robot gathers data in a single area over multiple traversals [30], [32], [36], on-policy data collection [36], access to a reward signal beyond disengagement [30], perform their evaluations in the training environment [30], [36], or are only demonstrated in simulation [31], [35]. In contrast, our LaND method is safe because it leverages the existing human-safety driver, learns from off-policy data, does not require retraversing an area multiple times, learns directly from whether the robot is engaged or disengaged, and evaluate in novel, never-beforeseen real-world environments.…”