Autonomous robots must navigate reliably in unknown environments even under compromised exteroceptive perception, or perception failures. Such failures often occur when harsh environments lead to degraded sensing, or when the perception algorithm misinterprets the scene due to limited generalization. In this paper, we model perception failures as invisible obstacles and pits, and train a reinforcement learning (RL) based local navigation policy to guide our legged robot. Unlike previous works relying on heuristics and anomaly detection to update navigational information, we train our navigation policy to reconstruct the environment information in the latent space from corrupted perception and react to perception failures end-to-end. To this end, we incorporate both proprioception and exteroception into our policy inputs, thereby enabling the policy to sense collisions on different body parts and pits, prompting corresponding reactions. We validate our approach in simulation and on the real quadruped robot ANYmal running in real-time (<10 ms CPU inference). In a quantitative comparison with existing heuristic-based locally reactive planners, our policy increases the success rate over 30% when facing perception failures. Project Page: https://bit.ly/45NBTuh.

翻译：自主机器人即使在外部感知受损或感知失败的情况下，也必须在未知环境中可靠导航。此类失败常发生在恶劣环境导致传感性能下降，或感知算法因泛化能力不足而错误解读场景时。本文中将感知失败建模为隐形障碍物和坑洞，并训练基于强化学习的局部导航策略以引导腿式机器人。与依赖启发式方法和异常检测更新导航信息的先前工作不同，我们训练导航策略在潜在空间中从受损感知重构环境信息，并端到端地响应感知失败。为此，我们将本体感知和外部感知均纳入策略输入，使策略能够感知不同身体部位的碰撞和坑洞，从而触发相应反应。我们在仿真场景和真实四足机器人ANYmal上验证了该方法，实现了实时运行（CPU推理时间<10毫秒）。与现有基于启发式的局部反应式规划器进行定量比较时，我们的策略在面临感知失败时成功率提升超过30%。项目页面：https://bit.ly/45NBTuh。