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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
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<meta name="description" content="MIEL">
<meta property="og:title" content="MIEL"/>
<meta property="og:description" content="Project page for Multimodal Interactive Exophora resolution with user Localization."/>
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<h1 class="title is-1 publication-title">Take That for Me: Multimodal Exophora Resolution with Interactive Questioning for Ambiguous Out-of-View Instructions</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
Akira Oyama<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>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<sup>1</sup></a></span>
<span class="author-block">
<a href="https://scholar.google.co.jp/citations?user=Y4qjYvMAAAAJ&hl=ja&oi=ao" target="_blank">Yoshinobu Hagiwara<sup>2,1</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>3,1</sup></a></span>
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<span class="author-block">
<sup>1</sup>Ritsumeikan University,
<sup>2</sup>Soka University,
<sup>3</sup>Kyoto University
<br><span class="publication-awards">Accepted at IEEE RO-MAN 2025</span>
</span>
<span class="eql-cntrb"><small><br><sup>*</sup>Corresponding Author</small></span>
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<a href="https://ieeexplore.ieee.org/document/11217563" target="_blank"
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Paper
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<a href="https://speakerdeck.com/shoichi_hasegawa/ro-man25-take-that-for-me-multimodal-exophora-resolution-with-interactive-questioning-for-ambiguous-out-of-view-instructions" target="_blank"
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<span>arXiv</span>
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</div>
</section>
<!-- Teaser video-->
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<h2 class="title is-3">Abstract</h2>
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<p>
Daily life support robots must interpret ambiguous verbal instructions involving demonstratives such as ``Bring me that cup,'' even when objects or users are out of the robot's view.
Existing approaches to exophora resolution primarily rely on visual data and thus fail in real-world scenarios where the object or user is not visible.
We propose Multimodal Interactive Exophora resolution with user Localization (MIEL), which is a multimodal exophora resolution framework leveraging sound source localization (SSL), semantic mapping, visual-language models (VLMs), and interactive questioning with GPT-4o.
Our approach first constructs a semantic map of the environment, integrating linguistic queries and skeletal detection to estimate candidate target objects.
SSL is utilized to orient the robot toward users who are initially outside its visual field, enabling accurate identification of user gestures and pointing directions.
When ambiguities remain, the robot proactively interacts with the user, employing GPT-4o to formulate clarifying questions.
Experiments in a real-world environment showed results that were approximately 1.3 times better when the user was visible to the robot and 2.0 times better when the user was not visible to the robot, compared to the methods without SSL and interactive questioning.
</p>
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<h2 class="title is-3">Overview</h2>
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</div>
<h2 class="subtitle">
This study addresses situations where a user gives a robot an ambiguous
command with a demonstrative (e.g., “Take that for me”), especially
when the user is outside the robot’s view and non-verbal cues like pointing
are unavailable, making it hard to identify what “that” refers to (Left).
To address this issue, (a) Utilizing sound source localization to estimate
the direction of the user and obtain pointing information. (b) Narrowing
down candidate objects by exophora resolution. (c) Supplementing missing
information by asking questions using GPT-4o.
</h2>
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<h2 class="title is-3">Multimodal Interactive Exophora resolution with user Localization (MIEL)</h2>
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<div class="image-container">
<img src="static/images/proposed_method.svg" alt="image of model" style="display: block; margin: 0 auto; width: 70%; height: auto;" />
</div>
<h2 class="subtitle">
An overview of the MIEL. Initially, linguistic queries from the user, including demonstratives, are processed into semantic and visual representations
using SentenceBERT and CLIP encoders. A semantic map provides object locations and visual data, while skeletal detection and SSL determine user direction
and pointing gestures. Three estimators generate candidate target object probabilities. If the initial identification is ambiguous, GPT-4o engages the user
interactively through targeted questions, refining object identification and ensuring robust exophora resolution.
</h2>
</div>
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</section>
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<h2 class="title is-3">Linguistic Query-based Estimator</h2>
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<img src="static/images/linguistic_query_estimator.svg" alt="image of model" style="display: block; margin: 0 auto; width: 70%; height: auto;" />
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<h2 class="subtitle">
The linguistic query-based estimator computes the probability of each candidate object being the target by multiplying and normalizing two cosine similarities:
one between object label features and SentenceBERT-encoded linguistic queries, and the other between visual features and CLIP-encoded queries, all derived from the semantic map.
</h2>
</div>
</div>
</div>
</section>
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<div class="hero-body">
<div class="container">
<h2 class="title is-3">Demonstrative Region-based Estimator</h2>
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<img src="static/images/demonstrative_estimator.svg" alt="image of model" style="display: block; margin: 0 auto; width: 70%; height: auto;" />
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<h2 class="subtitle">
Each region is represented by a 3D Gaussian distribution using the different characters of the demonstrative series. Robot obtains eye and wrist coordinates by MediaPipe (skeleton detector). Coordinates (eye and wrist) are used as parameters of a 3D Gaussian distribution
</h2>
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</div>
</div>
</section>
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<section class="hero is-small">
<div class="hero-body">
<div class="container">
<h2 class="title is-3">Pointing Direction-based Estimator</h2>
<!-- <div id="results-carousel" class="carousel results-carousel"> -->
<div class="item">
<div class="image-container">
<img src="static/images/pointing_estimator.svg" alt="image of model" style="display: block; margin: 0 auto; width: 70%; height: auto;" />
</div>
<h2 class="subtitle">
After skeleton detection, the robot calculates the angle between the pointing (blue) and the object direction (red). Then, the robot outputs the probability density of a 2D von Mises distribution with the obtained angles. Our model can deal with situations where there are no users in the robot's field of vision.
</h2>
</div>
</div>
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</section>
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<section class="hero is-small">
<div class="hero-body">
<div class="container">
<h2 class="title is-3">Interactive Questioning Module</h2>
<!-- <div id="results-carousel" class="carousel results-carousel"> -->
<div class="item">
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<img src="static/images/interactive.svg" alt="image of model" style="display: block; margin: 0 auto; width: 70%; height: auto;" />
</div>
<h2 class="subtitle">
An overview of the module for interactive questioning. The red arrows represent the process flow when the target object is successfully
identified, while the blue arrows indicate the process flow when the target object remains unidentified.
</h2>
</div>
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</section>
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<h2 class="title is-3">Global localization</h2>
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<video poster="" id="video1" autoplay controls muted loop height="100%">
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<h2 class="title is-3">Level of Ambiguous Instructions for Experiments</h2>
<h2 class="subtitle">
Divided queries into three levels, and manually created each of the 30 queries in Japanese.
</h2>
<div class="image-container">
<img src="static/images/query.svg" alt="image of model" style="display: block; margin: 0 auto; width: 70%; height: auto;" />
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<h2 class="title is-3">Result-1: Case of User is Visible from the Robot’s Position</h2>
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<img src="static/images/visible_result.png" alt="image of model" class="center-image" style="max-width: 80%; height: auto;"/>
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<h2 class="subtitle">
We evaluated MIEL under conditions where the user was visible from the robot's initial position.
MIEL outperformed ECRAP with a 1.3× higher SR (success rate) overall and over 2× higher SR for level-3 queries lacking object class information, highlighting the benefit of interactive clarification.
While MIEL achieved lower SRs than humans (30–60% lower), the gap is attributed to challenges in generating effective questions and the absence of gaze cues, which humans used alongside pointing.
</h2>
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<div class="hero-body">
<div class="container">
<h2 class="title is-3">Result-2: Case of User is Invisible from the Robot’s Position</h2>
<div class="image-container">
<img src="static/images/non_visible_result.png" alt="image of model" class="center-image" style="max-width: 80%; height: auto;"/>
</div>
<h2 class="subtitle">
When the user is not initially visible, MIEL achieves 3× higher SR than ECRAP by using SSL to locate the user and obtain skeletal data.
This allows MIEL to maintain SR comparable to visible-user conditions, whereas ECRAP's performance drops significantly due to its reliance on limited query information.
</h2>
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<div class="hero-body">
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<h2 class="title is-3">Result-3: The Ablation Study for Exophora Resolution. L1, L2, and L3 Indicate Levels.</h2>
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<img src="static/images/ablation_result.png" alt="image of model" class="center-image" style="max-width: 80%; height: auto;"/>
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<h2 class="subtitle">
The ablation study shows that Q&A significantly improves SR for higher-level queries, with level-3 SR more than doubling when Q&A is used.
SSL also boosts SR by 1.7×, confirming its role in acquiring user skeletal data when initially unavailable.
Together, these components are crucial for handling ambiguous queries in MIEL.
</h2>
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</section>
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<h2 class="title is-3">Prompts</h2>
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<img src="static/images/prompt_identfy_based_exophora.svg" alt="image of model" style="display: block; margin: 0 auto; width: 70%; height: auto;" />
</div>
<h3 class="title is-5" style="text-align: center; margin-top: 1em; margin-bottom: 2em;">
(1) Prompts for Identifying Target Objects based on the Results of Exophora Resolution
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<img src="static/images/prompt_ask_user.svg" alt="image of model" style="display: block; margin: 0 auto; width: 70%; height: auto;" />
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<h3 class="title is-5" style="text-align: center; margin-top: 1em; margin-bottom: 2em;">
(2) Prompts for Generation a Question
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<img src="static/images/prompt_identify_base_user_answer.svg" alt="image of model" style="display: block; margin: 0 auto; width: 70%; height: auto;" />
</div>
<h3 class="title is-5" style="text-align: center; margin-top: 1em; margin-bottom: 2em;">
(3) Prompts for Identifying Target Objects based on User's Answering
</h3>
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<pre><code>
@inproceedings{oyama2025miel,
author={Oyama, Akira and Hasegawa, Shoichi and Taniguchi, Akira and Hagiwara, Yoshinobu and Taniguchi, Tadahiro},
title={Take That for Me: Multimodal Exophora Resolution with Interactive Questioning for Ambiguous Out-of-View Instructions},
booktitle = {{IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)}},
year = {2025, accepted}
}
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<li><a href="https://ieeexplore.ieee.org/abstract/document/10309487" target="_blank" rel="noopener noreferrer">Exophora Resolution Framework</a></li>
<li><a href="https://emergentsystemlabstudent.github.io/ECRAP/" target="_blank" rel="noopener noreferrer">ECRAP</a></li>
<li><a href="https://emergentsystemlabstudent.github.io/PointingImgEst/" target="_blank" rel="noopener noreferrer">PointingImgEst</a></li>
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<li><a href="http://www.em.ci.ritsumei.ac.jp/" target="_blank" rel="noopener noreferrer">Emergent Systems Laboratory (Ritsumeikan University)</a></li>
<li><a href="https://sites.google.com/view/tanichu-lab-ku/" target="_blank" rel="noopener noreferrer">Taniguchi Laboratory (Kyoto University)</a></li>
<li><a href="https://sites.google.com/soka-u.jp/hagiwara/home" target="_blank" rel="noopener noreferrer">Hagiwara Laboratory (Soka University)</a></li>
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This work was supported by JSPS KAKENHI Grants-in-Aid for Scientific Research (Grant Numbers JP23K16975, JP22K12212), JST Moonshot Research & Development Program (Grant Number JPMJMS2011).
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