English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

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