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Paper Abstract and Keywords
Presentation 2026-02-19 13:30
GCL: Group Competitive Learning for Socially Aware Robot Navigation
Xinyu Zhang, Tomohito Kawabata, Zishuo Wang, Zhuonan Liu, Ling Xiao (Hokkaido Univ.) ITS2025-49 IE2025-64
Abstract (in Japanese) (See Japanese page) 
(in English) Social robot navigation requires an understanding of complex scene semantics and strict adherence to human social norms. Although large vision language models (LVLMs) have demonstrated remarkable performance, their high computational cost render them impractical for deployment on resource-constrained robotic platforms. Existing lightweight models often suffer from limited generalization capabilities and struggle to produce stable, socially compliant action decisions. To address these challenges, this paper proposes a Group Competitive Learning (GCL) framework. Our approach leverages two lightweight pretrained VLMs, Qwen2.5-VL-3B and Qwen3-VL-4B, as backbone networks and introduces a competitive cooperative training strategy. This strategy jointly minimizes inter model discrepancies and deviations from ground truth actions, thereby guiding the models toward generating consistent and socially appropriate action commands (e.g., move forward, stop, turn). Specifically, we present a novel group competition learning loss function that integrates competitive learning between models with supervised learning by combining a global semantic loss, distributional regularization loss and a supervision loss. In addition, we propose an asymmetric group optimization that maximizes the performance of two heterogeneous models by introducing asymmetric learning rates. Experimental results on established social navigation benchmarks demonstrate that the proposed method significantly enhances both social compliance and action stability while achieving optimal performance for both models.
Keyword (in Japanese) (See Japanese page) 
(in English) Social robot navigation / Small vision language models / Competitive learning / Human-robot interaction / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 356, IE2025-64, pp. 55-60, Feb. 2026.
Paper # IE2025-64 
Date of Issue 2026-02-12 (ITS, IE) 
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 ITS2025-49 IE2025-64

Conference Information
Committee IE ITS ITE-MMS ITE-ME ITE-AIT ITE-SIP  
Conference Date 2026-02-19 - 2026-02-20 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IE 
Conference Code 2026-02-IE-ITS-MMS-ME-AIT-SIP 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) GCL: Group Competitive Learning for Socially Aware Robot Navigation 
Sub Title (in English)  
Keyword(1) Social robot navigation  
Keyword(2) Small vision language models  
Keyword(3) Competitive learning  
Keyword(4) Human-robot interaction  
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1st Author's Name Xinyu Zhang  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Tomohito Kawabata  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Zishuo Wang  
3rd Author's Affiliation Hokkaido University (Hokkaido Univ.)
4th Author's Name Zhuonan Liu  
4th Author's Affiliation Hokkaido University (Hokkaido Univ.)
5th Author's Name Ling Xiao  
5th Author's Affiliation Hokkaido University (Hokkaido Univ.)
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Speaker Author-1 
Date Time 2026-02-19 13:30:00 
Presentation Time 15 minutes 
Registration for IE 
Paper # ITS2025-49, IE2025-64 
Volume (vol) vol.125 
Number (no) no.355(ITS), no.356(IE) 
Page pp.55-60 
#Pages 6 
Date of Issue 2026-02-12 (ITS, IE) 

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