Adaptive Companionship for Group-Following Robots: Handling Dynamically Changing Group Formations
This paper proposes an adaptive group-accompaniment method for social robots based on Vision-Language Models (VLMs). It leverages VLM semantic reasoning to infer companion positions, maintain social distances, and understand group dynamics, combined with a Model Predictive Path Integral (MPPI) controller for stability. Experiments show a 15% improvement in success rate and a 25% reduction in collision rate, with user study perceiving behaviors as natural and socially appropriate.
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[Submitted on 1 Jul 2026]
Title:Adaptive Companionship for Group-Following Robots: Handling Dynamically Changing Group Formations
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Abstract:Accompanying a group of humans is an essential aspect of developing human-like social cognition in robots. However, human groups typically do not follow fixed formations, which poses significant challenges for robots in maintaining natural companionship behaviors. In this paper, we propose an adaptive group-accompaniment method for social robots based on Vision-Language Models (VLMs), leveraging their semantic reasoning capabilities to infer companion positions, maintain social distances, and understand group dynamics. The members of the group are first detected, and a perceptual module generates visual representations of the interaction group space as input to the VLM, which is then combined with a Model Predictive Path Integral (MPPI) controller to ensure stability and safety. Experimental evaluations across five scenarios show that the proposed method enables robots to accompany the group effectively, demonstrating a 15\% improvement in success rate and a 25\% reduction in collision rate compared to baseline approaches. Additionally, a user study indicates that the generated companionship behaviors are perceived as natural and socially appropriate.
Comments: Accepted to the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
Cite as: arXiv:2607.01287 [cs.RO]
(or arXiv:2607.01287v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.01287
arXiv-issued DOI via DataCite
Submission history
From: Cong-Thanh Vu [view email] [v1] Wed, 1 Jul 2026 10:24:14 UTC (7,369 KB)
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