# Adaptive Companionship for Group-Following Robots: Handling Dynamically Changing Group Formations

arXiv: https://arxiv.org/abs/2607.01287 · HTML: https://mlsanity.com/paper/2607.01287/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 31 references that are in the map.
- First posted on arXiv 1 Jul 2026.

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

- [Towards Adaptive Interaction Strategies for Human Companion Robot via Deep Reinforcement Learning](https://mlsanity.com/paper/2609.25031/) (2026)

## Abstract (arXiv)

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.