MCCE/index.html at master · EmergentSystemLabStudent/MCCE · GitHub

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          <h1 class="title is-1 publication-title">Integrating Multimodal Communication and Comprehension Evaluation during <br>Human-Robot Collaboration for <br>Increased Reliability of Foundation Model-based Task Planning Systems</h1>
          <div class="is-size-5 publication-authors">
            <!-- Paper authors -->
            <span class="author-block">
              Eden Martin<sup>1</sup>,</span>
            <span class="author-block">
              <a href="https://scholar.google.co.jp/citations?user=KPxSCJUAAAAJ&hl=ja&oi=ao" target="_blank">Shoichi Hasegawa<sup>2</sup>,</span>
            <span class="author-block">
              <a href="https://scholar.google.co.jp/citations?user=4zk0t8UAAAAJ&hl=ja&oi=ao" target="_blank">Jorge Solis<sup>3</sup>,</a></span>
            <span class="author-block">
              <a href="https://scholar.google.co.jp/citations?user=H9pGN70AAAAJ&hl=ja&oi=ao" target="_blank">Benoit Macq<sup>1</sup>,</a></span>
            <span class="author-block">
              <a href="https://scholar.google.co.jp/citations?user=ojhYFjkAAAAJ&hl=ja&oi=ao" target="_blank">Renaud Ronsse<sup>1</sup></a></span>
            <span class="author-block">
              <a href="https://scholar.google.co.jp/citations?user=IKkMDxEAAAAJ&hl=ja&oi=ao" target="_blank">Gustavo Alfonso Garcia Ricardez<sup>2</sup></a></span>
            <span class="author-block">
              <a href="https://scholar.google.co.jp/citations?user=tsm7qaQAAAAJ&hl=ja&oi=ao" target="_blank">Lotfi El Hafi<sup>2,*</sup></a></span>
            <span class="author-block">
              <a href="https://scholar.google.co.jp/citations?user=dPOCLQEAAAAJ&hl=ja&oi=ao" target="_blank">Tadahiro Taniguchi<sup>2,4</sup></a></span>
          </div>

          <div class="is-size-5 publication-authors">
            <span class="author-block">
              <sup>1</sup>Universitè catholique de Louvain (UCLouvain),
              <sup>2</sup>Ritsumeikan University,<br>
              <sup>3</sup>Karlstad University,
              <sup>4</sup>Kyoto University
              <br><span>Accepted at IEEE/SICE SII 2025.</span>
            </span>
            <span class="eql-cntrb"><small><br><sup>*</sup>Corresponding Author</small></span>
          </div>

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        <h2 class="title is-3">Abstract</h2>
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          <p>
            Foundation models provide the adaptability needed in robotics but often require explicit tasks or human
            verification due to potential unreliability in their responses, complicating human-robot collaboration (HRC).
            To enhance the reliability of such task-planning systems, we propose 1) an adaptive task-planning system for
            HRC that reliably performs non-predefined tasks implicitly instructed through HRC, and 2) an integrated system
            combining multimodal large language model (LLM)-based task planning with multimodal communication of human intention
            to increase the HRC success rate and comfort. The proposed system integrates GPT-4V for adaptive task planning and
            comprehension evaluation during HRC with multimodal communication of human intention through speech and deictic gestures.
            Four pick-and-place tasks of gradually increasing difficulty were used in three experiments, each evaluating a key aspect
            of the proposed system: task planning, comprehension evaluation, and multimodal communication. The quantitative results
            show that the proposed system can interpret implicitly instructed tabletop pick-and-place tasks through HRC, providing the
            next object to pick and the correct position to place it, achieving a mean success rate of 0.80. Additionally, the system
            can evaluate its comprehension of three of the four tasks with an average precision of 0.87. The qualitative results show
            that multimodal communication not only significantly enhances the success rate but also the feelings of trust and control,
            willingness to use again, and sense of collaboration during HRC.
          </p>
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      <h2 class="title is-3">Overview</h2>
      <!-- <div id="results-carousel" class="carousel results-carousel"> -->
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            <img src="static/images/overview_martin.svg" alt="image of model" class="center-image"/>
          </div>
          <h2 class="subtitle">
            The proposed system enhances the reliability of foundation model-based task planning in HRC (blue area)
            through comprehension evaluation using an inferred confidence level by the robot agent (green area) and
            multimodal communication using deictic gestures and speech instructions by the human agent (orange area).
          </h2>
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      <h2 class="title is-3">Multimodal Communication and Comprehension Assessment for Task Planning</h2>
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            <img src="static/images/proposed_martin.svg" alt="image of model" class="center-image"/>
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          <h2 class="subtitle">
            Implementation of the proposed system. The colored areas represent the four modules
            of the proposed system: the cloud API, the sensors, the computer, and the robot.
            The black arrows indicate the flow of information, while the red lines denote
            the types of connections between the modules.
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      <h2 class="title is-3">Experiment1: Task Planning</h2>
        <div class="image-container">
          <img src="static/images/exp1_task_planning.png" alt="image of model" class="center-image" style="max-width: 80%; height: auto;"/>
        </div>

        <div class="image-container">
          <img src="static/images/result1_task_planning.png" alt="image of model" class="center-image" style="max-width: 80%; height: auto;"/>
          <!-- <iframe  src="static/pdfs/model2.pdf" width="100%" height="550"></iframe> -->
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      <h2 class="title is-3">Experiment2: Comprehension Evaluation</h2>
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          <img src="static/images/exp2_comprehension_evaluation.png" alt="image of model" class="center-image" style="max-width: 80%; height: auto;"/>
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          <img src="static/images/result2_comprehension_evaluation.png" alt="image of model" class="center-image" style="max-width: 80%; height: auto;"/>
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      <h2 class="title is-3">Experiment3: Multimodal Communication</h2>
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          <img src="static/images/exp3_multimodal_communication.png" alt="image of model" class="center-image" style="max-width: 80%; height: auto;"/>
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          <img src="static/images/result3_multimodal_communication.png" alt="image of model" class="center-image" style="max-width: 80%; height: auto;"/>
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    <h2 class="title">BibTeX</h2>
    <!-- <pre><code>BibTex Code Here</code></pre> -->
    <pre><code>
      @inproceedings{martin2025mcce,
        author={Martin, Eden  and Hasegawa, Shoichi  and Solis, Jorge  and Macq, Benoit  and Ronsse, Renaud and Garcia Ricardez, Gustavo Alfonso and El Hafi, Lotfi and Taniguchi, Tadahiro},
        title={Integrating Multimodal Communication and Comprehension Evaluation during Human-Robot Collaboration for Increased Reliability of Foundation Model-based Task Planning Systems},
        booktitle={IEEE/SICE International Symposium on System Integrations (SII)},
        year={2025, accepted}
      }
    </code></pre>
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<!--End BibTex citation -->

<!--Related Research -->
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    <h2 class="title">Related Research</h2>
    <ul>
      <li><a href="https://t.co/fUfYedw4QI" target="_blank" rel="noopener noreferrer">GPTAlly</a></li>
      <li><a href="https://emergentsystemlabstudent.github.io/MultiViewRetrieve/" target="_blank" rel="noopener noreferrer">SimView</a></li>
      <li><a href="https://emergentsystemlabstudent.github.io/DomainBridgingNav/" target="_blank" rel="noopener noreferrer">CrossIA</a></li>
      <li><a href="https://tomochika-ishikawa.github.io/Active-SpCoSLAM/" target="_blank" rel="noopener noreferrer">Active SpCoSLAM</a></li>
      <li><a href="https://emergentsystemlabstudent.github.io/PointingImgEst/" target="_blank" rel="noopener noreferrer">PointingImgEst</a></li>
      <li><a href="https://ieeexplore.ieee.org/abstract/document/10309487" target="_blank" rel="noopener noreferrer">Exophora Resolution Framework</a></li>
    </ul>
  </div>
</section>
<!--End Related Research -->

<!--Laboratory Information -->
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    <h2 class="title">Laboratory Information</h2>
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      <li><a href="http://www.em.ci.ritsumei.ac.jp/" target="_blank" rel="noopener noreferrer">Emergent Systems Laboratory</a></li>
      <li><a href="https://www.youtube.com/@tarosouhatsu494/videos" target="_blank" rel="noopener noreferrer">Demonstration Videos of the Laboratory</a></li>
    </ul>
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      This work was supported by JST Moonshot Research \& Development Program (Grant Number JPMJMS2011).
    </p>
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