具弹性的足式机器人局部导航：学习在感知受损情况下端到端穿越环境
Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End
AI总结：
针对自主机器人外感受知受损或失效的导航难题，提出将感知失效建模为隐形障碍与凹坑、融合本体与外感受知的强化学习端到端局部导航策略，在仿真和ANYmal四足机器人上验证，感知失效场景下成功率较启发式方法提升超30%。
摘要：
自主机器人必须能在未知环境中可靠导航，即便在外感受知受损或感知失效的情况下也应如此。这类失效常发生于恶劣环境导致传感性能下降，或是感知算法因泛化能力有限而对场景产生误判时。在本文中，我们将感知失效建模为不可见的障碍物与凹坑，并训练一种基于强化学习（reinforcement learning, RL）的局部导航策略来引导我们的足式机器人。与以往依赖启发式方法和异常检测来更新导航信息的研究不同，我们训练的导航策略能够从被破坏的感知数据中在隐空间重构环境信息，并以端到端的方式对感知失效做出反应。为此，我们将本体感知与外感受知同时纳入策略输入，使策略能够感知身体不同部位的碰撞与凹坑，进而触发相应的反应。我们在仿真环境以及实时运行（CPU推理时间<10 ms）的真实四足机器人ANYmal上验证了所提方法。在与现有基于启发式的局部反应式规划器的定量对比中，我们的策略在面临感知失效时将成功率提升了30%以上。项目页面：https://bit.ly/45NBTuh。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
