# Take That for Me: Multimodal Exophora Resolution with Interactive Questioning for Ambiguous Out-of-View Instructions

arXiv: https://arxiv.org/abs/2508.16143 · HTML: https://mlsanity.com/paper/2508.16143/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Builds on (most cited first, 17 of 17)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Detecting Twenty-thousand Classes using Image-level Supervision](https://mlsanity.com/paper/2201.02605/) (2022)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners](https://mlsanity.com/paper/2307.01928/) (2023)
- [Open-vocabulary Queryable Scene Representations for Real World Planning](https://mlsanity.com/paper/2209.09874/) (2022)
- [CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents](https://mlsanity.com/paper/2306.10376/) (2023)
- [Dual Attention Networks for Visual Reference Resolution in Visual Dialog](https://mlsanity.com/paper/1902.09368/) (2019)
- [What You See is What You Get: Visual Pronoun Coreference Resolution in Dialogues](https://mlsanity.com/paper/1909.00421/) (2019)
- [Gesture-Informed Robot Assistance via Foundation Models](https://mlsanity.com/paper/2309.02721/) (2023)
- [VD-PCR: Improving Visual Dialog with Pronoun Coreference Resolution](https://mlsanity.com/paper/2205.14693/) (2022)
- [A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous Japanese Questions](https://mlsanity.com/paper/2403.17545/) (2024)
- [J-CRe3: A Japanese Conversation Dataset for Real-world Reference Resolution](https://mlsanity.com/paper/2403.19259/) (2024)
- [VGPN: Voice-Guided Pointing Robot Navigation for Humans](https://mlsanity.com/paper/2004.01600/) (2020)
- [Exophoric Pronoun Resolution in Dialogues with Topic Regularization](https://mlsanity.com/paper/2109.04787/) (2021)

## Built on by (most cited first, 1 of 1)

- [Toward Ownership Understanding of Objects: Active Question Generation with Large Language Model and Probabilistic Generative Model](https://mlsanity.com/paper/2509.12754/) (2025)

## Abstract (arXiv)

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 and estimates candidate objects from a linguistic query with the user's skeletal data. 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. The project website is https://emergentsystemlabstudent.github.io/MIEL/.