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

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<!DOCTYPE html>
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  <!-- TODO: Add 5-10 relevant keywords for your research area -->
  <meta name="keywords" content="Action Planning, Large Language Models, Semantic Mapping, Service Robot, Spatial Concepts">
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  <meta name="author" content="Shoichi Hasegawa, Yoshinobu Hagiwara, Akira Taniguchi, Lotfi El Hafi, Tadahiro Taniguchi">
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  <meta property="article:published_time" content="2024-01-01T00:00:00.000Z">
  <meta property="article:author" content="Shoichi Hasegawa">
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            <h5>Take That for Me: Multimodal Exophora Resolution with Interactive Questioning for Ambiguous Out-of-View Instructions</h5>
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            <p>Robust exophora resolution with interactive questioning and sound source localization.</p>
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            <span class="work-venue">IEEE RO-MAN 2025</span>
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            <h5>Toward Ownership Understanding of Objects: Active Question Generation with Large Language Model and Probabilistic Generative Model</h5>
            <p>Acquisition of object ownership information with LLMs and probabilistic generative models.</p>
            <span class="work-venue">AROB-ISBC 2026</span>
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            <h5>Multi-Robot Task Planning for Multi-Object Retrieval Tasks with Distributed On-Site Knowledge via Large Language Models</h5>
            <p>LLM-based multi-robot coordination with distributed on-site knowledge toward ambiguous instructions</p>
            <span class="work-venue">AROB-ISBC 2026</span>
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            <!-- TODO: Replace with your paper title -->
            <h1 class="title is-1 publication-title">Spatial Concepts-Based Prompts <br>With Large Language Models <br>for Robot Action Planning</h1>
            <div class="is-size-5 publication-authors">
              <!-- TODO: Replace with your paper authors and their personal links -->
              <span class="author-block">
                <a href="https://scholar.google.co.jp/citations?user=KPxSCJUAAAAJ&hl=ja&oi=ao" target="_blank">Shoichi Hasegawa</a><sup>1,*</sup>,</span>
              <span class="author-block">
                <a href="https://scholar.google.co.jp/citations?user=Y4qjYvMAAAAJ&hl=ja&oi=ao" target="_blank">Yoshinobu Hagiwara</a><sup>2,1</sup>,</span>
              <span class="author-block">
                <a href="https://scholar.google.co.jp/citations?user=jtB7J0AAAAAJ&hl=ja&oi=ao" target="_blank">Akira Taniguchi</a><sup>1</sup>,</span>
              <br>
              <span class="author-block">
                <a href="https://scholar.google.co.jp/citations?user=tsm7qaQAAAAJ&hl=ja&oi=ao" target="_blank">Lotfi El Hafi</a><sup>1</sup>, and</span>
              <span class="author-block">
                <a href="https://scholar.google.co.jp/citations?user=dPOCLQEAAAAJ&hl=ja&oi=ao" target="_blank">Tadahiro Taniguchi</a><sup>3,1</sup></span>
              </span>
            </div>

                  <div class="is-size-5 publication-authors">
                    <!-- TODO: Replace with your institution and conference/journal info -->
                    <span class="author-block">
                      <sup>1</sup>Ritsumeikan Univ.
                      <sup>2</sup>Soka Univ.
                      <sup>3</sup>Kyoto Univ.
                      <br>
                      <span class="publication-title">IEEE Access</span>
                    </span>
                    <!-- TODO: Remove this line if no equal contribution -->
                    <span class="eql-cntrb"><small><br><sup>*</sup></small>Corresponding Author</span>
                  </div>

                  <div class="column has-text-centered">
                    <div class="publication-links">
                         <!-- TODO: Update with your arXiv paper ID -->
                      <span class="link-block">
                        <a href="https://ieeexplore.ieee.org/document/11311990" target="_blank"
                        class="external-link button is-normal is-rounded is-dark">
                        <span class="icon">
                          <i class="fas fa-file-pdf"></i>
                        </span>
                        <span>Paper</span>
                      </a>
                    </span>

                    <!-- TODO: Add your supplementary material PDF or remove this section -->
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                      <span>Supplementary</span>
                    </a>
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                    <a href="https://github.com/EmergentSystemLabStudent/em_spcorap" target="_blank"
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                    <span>Code</span>
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                </span>

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</section>


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        <h2 class="title is-3">Abstract</h2>
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          <!-- TODO: Replace with your paper abstract -->
          <p>
            Daily life instructions often include contextual cues related not to the object itself, but to its surrounding environment (e.g., objects placed around the target).
            Therefore, it is essential for service robots to interpret such surrounding information and translate it into action plans.
            Recently, there has been growing interest in using large language models (LLMs) to generate actions from instructions.
            Among these approaches, some studies explored leveraging semantic maps to better handle environmental context.
            However, previous studies face two limitations: 1) semantic maps are limited to object-level data, such as object class and position, and 2) since LLMs refer to semantic maps based on object classes, they cannot utilize the surrounding context, leading to misidentifications.
            To address them, we propose an action planning method that integrates the spatial concept model with LLMs.
            The spatial concept model categorizes observations and learns the parameters of probabilistic distributions.
            It then infers place names and object arrangements using Bayesian inference.
            By leveraging inference results as prompts, our method enables context-aware action planning.
            In the simulation, we designed object-search tasks using an open vocabulary within a household scenario in Gazebo.
            The robot received two types of instructions: 1) instructions that specify the target and its surrounding items, and 2) instructions that specify the target and its location.
            Our method achieved a success rate of over 0.8, outperforming baselines.
            On the other hand, real-world experiments revealed challenges in providing feedback when manipulation or detection failed.
          </p>
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        <h2 class="subtitle has-text-centered">
          Research Overview.
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          Proposed Model: SpCoRAP.
        </h2>
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  title={{Spatial Concepts-Based Prompts With Large Language Models for Robot Action Planning}},
  author={Hasegawa, Shoichi and Hagiwara, Yoshinobu and Taniguchi, Akira and El Hafi, Lotfi and Taniguchi, Tadahiro},
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