Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914055173701632 |
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| author | Ganai, Milan Sinha, Rohan Agia, Christopher Morton, Daniel Di Lillo, Luigi Pavone, Marco |
| author_facet | Ganai, Milan Sinha, Rohan Agia, Christopher Morton, Daniel Di Lillo, Luigi Pavone, Marco |
| contents | While foundation models offer promise toward improving robot safety in out-of-distribution (OOD) scenarios, how to effectively harness their generalist knowledge for real-time, dynamically feasible response remains a crucial problem. We present FORTRESS, a joint reasoning and planning framework that generates semantically safe fallback strategies to prevent safety-critical, OOD failures. At a low frequency under nominal operation, FORTRESS uses multi-modal foundation models to anticipate possible failure modes and identify safe fallback sets. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10547 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning Ganai, Milan Sinha, Rohan Agia, Christopher Morton, Daniel Di Lillo, Luigi Pavone, Marco Robotics Artificial Intelligence While foundation models offer promise toward improving robot safety in out-of-distribution (OOD) scenarios, how to effectively harness their generalist knowledge for real-time, dynamically feasible response remains a crucial problem. We present FORTRESS, a joint reasoning and planning framework that generates semantically safe fallback strategies to prevent safety-critical, OOD failures. At a low frequency under nominal operation, FORTRESS uses multi-modal foundation models to anticipate possible failure modes and identify safe fallback sets. 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. |
| title | Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2505.10547 |