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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Foundation models can provide robust high-level reasoning on appropriatesafety interventions in hazardous scenarios beyond a robot's training data,i.e. out-of-distribution (OOD) failures. However, due to the high inferencelatency of Large Vision and Language Models, current methods rely on manuallydefined intervention policies to enact fallbacks, thereby lacking the abilityto plan generalizable, semantically safe motions. To overcome these challengeswe present FORTRESS, a framework that generates and reasons about semanticallysafe fallback strategies in real time to prevent OOD failures. At a lowfrequency in nominal operations, FORTRESS uses multi-modal reasoners toidentify goals and anticipate failure modes. When a runtime monitor triggers afallback response, FORTRESS rapidly synthesizes plans to fallback goals whileinferring and avoiding semantically unsafe regions in real time. By bridgingopen-world, multi-modal reasoning with dynamics-aware planning, we eliminatethe need for hard-coded fallbacks and human safety interventions. FORTRESSoutperforms on-the-fly prompting of slow reasoning models in safetyclassification accuracy on synthetic benchmarks and real-world ANYmal robotdata, and further improves system safety and planning success in simulation andon quadrotor hardware for urban navigation.

Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning