Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End

TL;DR

This paper presents an end-to-end reinforcement learning approach for legged robot navigation that remains effective despite perception failures, by reconstructing environment information in latent space and integrating proprioception and exteroception.

Contribution

It introduces a novel RL-based local navigation policy that reconstructs environment data in latent space and reacts to perception failures without heuristics or anomaly detection.

Findings

Success rate increases over 30% under perception failures.

Effective in simulation and real-time on quadruped robot ANYmal.

Outperforms heuristic-based reactive planners.

Abstract

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.