知覚障害に強い脚式ロボットの局所ナビゲーション：劣化した知覚からのEnd-to-End学習
Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End
知覚失敗を不可視の障害物や穴としてモデル化し、固有感覚と外界感覚を統合した強化学習方策で脚式ロボットの局所ナビゲーションをEnd-to-Endで学習させ、知覚障害時でも成功率を30%以上向上させた。
著者: Jin Jin, Chong Zhang, Jonas Frey, Nikita Rudin, Matias Mattamala, Cesar Cadena, Marco Hutter
分類: cs.RO, cs.AI, cs.LG, cs.SY, eess.SY
原文アブストラクト
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.
