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Paper Abstract and Keywords
Presentation 2026-03-18 10:00
What Makes a Good Prompt for Social Robot Navigation?
Ling Xiao (Hokkaido Univ.), Toshihiko Yamasaki (UTokyo) IMQ2025-99 IE2025-176 MVE2025-106
Abstract (in Japanese) (See Japanese page) 
(in English) Language models are increasingly applied to social robot navigation, yet principled prompt design remains underexplored, especially for efficiency-oriented systems relying on small vision–language models (VLMs). Compared to large models, small VLMs have weaker decision-making capacity, making effective prompt design crucial for accurate navigation.
Inspired by cognitive theories of learning and motivation, we analyze prompt design along two dimensions: system guidance (action-focused, reasoning-oriented, and perception–reasoning prompts) and motivational framing (competition against humans, other AI systems, or past selves).
Experiments on two benchmarks yield three findings. First, non-finetuned GPT-4o performs best when competing against humans, whereas finetuned small VLMs achieve strongest results with past-self competition, with performance influenced by interactions among prompts, models, and datasets. Second, inappropriate system prompts can significantly degrade performance, even below direct finetuning. Third, while finetuning mainly improves semantic metrics, our prompts produce larger gains in action accuracy, indicating that they function primarily as decision-level constraints rather than representational enhancements.
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Reference Info. IEICE Tech. Rep., vol. 125, no. 411, MVE2025-106, pp. 442-447, March 2026.
Paper # MVE2025-106 
Date of Issue 2026-03-09 (IMQ, IE, MVE) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IMQ2025-99 IE2025-176 MVE2025-106

Conference Information
Committee CQ MVE IMQ IE  
Conference Date 2026-03-16 - 2026-03-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa-Sangyoushien-Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MVE 
Conference Code 2026-03-CQ-MVE-IMQ-IE 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) What Makes a Good Prompt for Social Robot Navigation? 
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1st Author's Name Ling Xiao  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Toshihiko Yamasaki  
2nd Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2026-03-18 10:00:00 
Presentation Time 20 minutes 
Registration for MVE 
Paper # IMQ2025-99, IE2025-176, MVE2025-106 
Volume (vol) vol.125 
Number (no) no.408(IMQ), no.410(IE), no.411(MVE) 
Page pp.442-447 
#Pages 6 
Date of Issue 2026-03-09 (IMQ, IE, MVE) 

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