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

Milan Ganai, Rohan Sinha, Christopher Agia, Daniel Morton, Marco Pavone

Keywords: Multi-modal Reasoning, Semantic Safety, OOD Reliability

TL;DR: An algorithm that prevents OOD Failures in open-world environments by rapidly generating semantically safe fallback plans through multi-modal reasoning.

Abstract: Foundation models can provide robust high-level reasoning on appropriate safety interventions in hazardous scenarios beyond a robot's training data, i.e. out-of-distribution (OOD) failures. However, due to the high inference latency of Large Vision and Language Models, current methods rely on manually defined intervention policies to enact fallbacks, thereby lacking the ability to plan generalizable, semantically safe motions. To overcome these challenges we present FORTRESS, a framework that generates and reasons about semantically safe fallback strategies in real time to prevent OOD failures. At a low frequency in nominal operations, FORTRESS uses multi-modal reasoners to identify goals and anticipate failure modes. When a runtime monitor triggers a fallback response, FORTRESS rapidly synthesizes plans to fallback goals while inferring and avoiding semantically unsafe regions in real time. By bridging open-world, multi-modal reasoning with dynamics-aware planning, we eliminate the need for hard-coded fallbacks and human safety interventions. FORTRESS outperforms on-the-fly prompting of slow reasoning models in safety classification accuracy on synthetic benchmarks and real-world ANYmal robot data, and further improves system safety and planning success in simulation and on quadrotor hardware for urban navigation. Website can be found at https://milanganai.github.io/fortress/.

Submission Number: 9