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<!DOCTYPE html>
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        content="Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning">
  <meta name="keywords" content="Multi-modal Reasoning in Robotics, OOD Safety, Fallback Synthesis">
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  <title>Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning</title>


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          <h1 class="title is-1 publication-title">Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning</h1>
		  <div class="is-size-5 publication-authors">
            <span class="author-block">
              <a href="https://milanganai.github.io/">Milan Ganai</a><sup>1</sup>,</span>
            <span class="author-block">
              <a href="https://rohansinha.nl/">Rohan Sinha</a><sup>1</sup>,</span>
            <span class="author-block">
              <a href="http://agiachris.github.io/">Christopher Agia</a><sup>1</sup>,
            </span>
            <span class="author-block">
              <a href="https://danielpmorton.github.io/">Daniel Morton</a><sup>1</sup>,
            </span>
            <span class="author-block">
              <a href="https://ieeexplore.ieee.org/author/37089761562">Luigi Di Lillo</a><sup>2</sup>,
            </span>
            <span class="author-block">
              <a href="https://profiles.stanford.edu/marco-pavone">Marco Pavone</a><sup>1,3</sup>
            </span>
          </div>

          <div class="is-size-5 publication-authors">
            <span class="author-block"><sup>1</sup>Stanford University,</span>
            <span class="author-block"><sup>2</sup>Swiss Re,</span>
            <span class="author-block"><sup>3</sup>NVIDIA Research</span>
          </div>

	  <div class="is-size-5 publication-authors" style="color: red; margin-top: 10px;">
            <span class="author-block"><b><u>Oral</u></b> at Conference on Robot Learning (CoRL) 2025</span>
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                  <span>arXiv</span>
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            <h2 class="subtitle has-text-centered">
		 <span class="dnerf">FORTRESS</span> is a framework that generates and reasons about semantically safe fallback strategies in real time to prevent out-of-distribution (OOD) failures in open-world environments. Our algorithm <span style="color:#f06292;">identifies goals</span> to implement semantic descriptions of fallback strategies, <span style="color:blue;">anticipates failures modes</span>, and <span style="color:red;">constructs semantic safety cost functions</span> that perceive dangerous state spaces. When a safety response is needed, FORTRESS rapidly produces semantically safe fallback plans.
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        <h2 class="title is-3">Abstract</h2>
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          <p>
            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.
          </p>
          <p>
            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.
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        <h2 class="title is-3">Quadrotor Drone Hardware Demo</h2>
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            <h2 class="title is-3">Semantic Safety Reasoning</h2>
            <p>
                We anticipate semantic failure modes using foundation model reasoners and calibrate
                semantic safety cost functions using text embedding models. We detect
                semantic OOD failures for an ANYmal robot in a room under construction.
            </p>
            <div class="embed-container">
                <iframe src="https://www.youtube.com/embed/xU-egPQjkFo?si=jKlSHZAsLVPdv23T" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen style="width: 100%; height: auto; aspect-ratio: 16 / 9;"></iframe>
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        <h2 class="title is-3">Fallback Strategy Goals</h2>
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                <p>
                    We translate semantic fallback strategies into relevant goal
                    points using a VLM. We show goals identified for the strategy
                    "land on building rooftop" for a drone navigating in an urban
                    setting in the CARLA simulator.
                </p>
               <img src="static/images/molmo_carla.png" class="img-responsive" style="width: 100%; height: auto;" />
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<section class="section" id="BibTeX">
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    <h2 class="title">BibTeX</h2>
    <pre><code>@inproceedings{pmlr-v305-ganai25a,
  title = 	 {Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning},
  author =       {Ganai, Milan and Sinha, Rohan and Agia, Christopher and Morton, Daniel and Lillo, Luigi Di and Pavone, Marco},
  booktitle = 	 {Proceedings of The 9th Conference on Robot Learning},
  pages = 	 {283--308},
  year = 	 {2025},
  editor = 	 {Lim, Joseph and Song, Shuran and Park, Hae-Won},
  volume = 	 {305},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {27--30 Sep},
  publisher =    {PMLR},
}
    </code></pre>
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