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            <h1 class="title is-1 publication-title">BabyBench: A Multimodal Benchmark of Infant Behaviors for Developmental AI</h1>
            <div class="is-size-5 publication-authors">
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              <span class="author-block">
                <a href="https://scholar.google.com/citations?user=pNDeaeQAAAAJ" target="_blank">Francisco M. López</a><sup>1</sup>,</span>
                <span class="author-block">
                <a href="https://scholar.google.com/citations?user=BbCeJhoAAAAJ" target="_blank">Valentin Marcel</a><sup>2</sup>,</span>
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                <a href="https://scholar.google.com/citations?user=pNW4eZAAAAAJ" target="_blank">Xavier Hinaut</a><sup>3</sup>,</span>
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                <a href="https://scholar.google.com/citations?user=AgEdsugAAAAJ" target="_blank">Jochen Triesch</a><sup>1</sup>,</span>
                <span class="author-block">
                <a href="https://scholar.google.com/citations?user=KFUbXYYAAAAJ" target="_blank">Matej Hoffmann</a><sup>2</sup></span>
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                  <div class="is-size-6 publication-authors">
                    <span class="author-block"><sup>1</sup>Frankfurt Institute for Advanced Studies;
                                               <sup>2</sup>Czech Technical University in Prague;
                                               <sup>3</sup>INRIA Bordeaux
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        <h2 class="title is-3">Abstract</h2>
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          <p>
            The field of Developmental Artificial Intelligence (AI) aims to replicate the development of human
            infants in artificial agents. It combines approaches from multiple fields, including
            developmental psychology, neuroscience, robotics, and machine learning. While AIs typically intend
            to maximize performance and efficiency regardless of how this is achieved, developmental AIs rather
            focus on the mechanisms that best recreate the learning processes of humans, mainly during the first
            years of life. As a consequence, developmental AIs must be subject to some fundamental constraints.
            First, they must have faithful physical limitations, such as a robotic or simulated embodiment with
            similar sensory and motor capabilities to infants. They must also have access to realistic
            environments to generate natural datasets for training. Likewise, they must rely on plausible
            learning mechanisms, including unsupervised, self-supervised, and intrinsically motivated learning.
            Beyond this, the evaluations performed on developmental AIs must appropriately reflect whether the
            learned behaviors match those of human infants, and likewise capture their failures. These
            evaluations must thoroughly assess the totality of the behavior and not merely the statistics of
            its outcome. Tests should thus include analysis of full-body trajectories, action planning,
            context-dependent adaptation, temporal dynamics, and other components of infant behavior beyond
            the scope of basic policies and representations.
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    <h2 class="title">BabyBench</h2>

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      <p>
        BabyBench is a developmental benchmark of behavioral milestones leveraging <a href="https://github.com/trieschlab/mimo">MIMo</a>, the multimodal infant model, with a unique modular approach: environments combine behaviors, scenes, sensory modalities, actuation models, and more.
      </p>
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          <h1>30+</h1>
          <h2>Behaviors</h2>
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      </div>

      <div class="counter_column">
        <div class="counter">
          <h1>20+</h1>
          <h2>Scenes</h2>
        </div>
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          <h1>40+</h1>
          <h2>DOF</h2>
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        <div class="counter">
          <h1>0-24</h1>
          <h2>Months embodiment</h2>
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          <h1>4</h1>
          <h2>Sensory modalities</h2>
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          <h1>3</h1>
          <h2>Actuation models</h2>
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          <h1>1000+</h1>
          <h2>Unique learning environments</h2>
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      <pre>
        <code>@misc{lopez2025babybench,
            title={BabyBench: A Multimodal Benchmark of Infant Behaviors for Developmental AI},
            author={Francisco M. López and Valentin Marcel and Xavier Hinaut and Jochen Triesch and Matej Hoffmann},
            year={2025},
        }</code>
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    <h2 class="title">Acknowledgements</h2>
<div class="logos">
<a href="https://fias.uni-frankfurt.de/en">
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</a>
<a href="https://www.cvut.cz/en">
<img src="static/images/cvut.jpg" alt="Czech Technical University in Prague" height="100"/>
</a>
<a href="https://www.inria.fr/en/inria-centre-university-bordeaux">
<img src="static/images/inria.jpg" alt="INRIA Bordeaux" height="100"/>
</a>
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  <a href="https://theadaptivemind.de">
  <img align="center" src="static/images/tam.png" alt="The Adaptive Mind" width="200" height="100"/>
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  <a href="https://neuroai-arena.github.io/">
  <img align="center" src="static/images/arena.png" alt="Abstract REpresentations in Neural Architectures (ARENA)" width="200" height="100"/>
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