Resilient Legged Local Navigation: Learning to Traverse with Compromised
  Perception End-to-End
Jin Jin Chong Zhang Jonas Frey Nikita Rudin Matias Mattamala Cesar Cadena Marco Hutter
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.