# The World According to a Social Robot -- Augmenting Human-Robot Dialogue With Vision Language Models

arXiv: https://arxiv.org/abs/2607.16318 · HTML: https://mlsanity.com/paper/2607.16318/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 15 Jul 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 8 of 8)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions](https://mlsanity.com/paper/2404.07214/) (2024)
- [VLM-Social-Nav: Socially Aware Robot Navigation through Scoring using Vision-Language Models](https://mlsanity.com/paper/2404.00210/) (2024)
- [Robotic Applications of Pre-Trained Vision-Language Models to Various Recognition Behaviors](https://mlsanity.com/paper/2303.05674/) (2023)
- [SocialNav-SUB: Benchmarking VLMs for Scene Understanding in Social Robot Navigation](https://mlsanity.com/paper/2509.08757/) (2025)
- [USER-VLM 360: Personalized Vision Language Models with User-aware Tuning for Social Human-Robot Interactions](https://mlsanity.com/paper/2502.10636/) (2025)
- [I Was Blind but Now I See: Implementing Vision-Enabled Dialogue in Social Robots](https://mlsanity.com/paper/2311.08957/) (2023)
- [TalkWithMachines: Enhancing Human-Robot Interaction for Interpretable Industrial Robotics Through Large/Vision Language Models](https://mlsanity.com/paper/2412.15462/) (2024)

## Abstract (arXiv)

Vision Language Models (VLMs) enable robots to visually perceive their environment as well as the actions and characteristics of their conversation partner or humans in collaboration. Especially for social robots deployed in everyday settings and for uncomplicated, natural use, it is essential that the robot has an understanding of situations that is appropriate to human customs. This paper presents initial experiences with the application of a Mistral AI language model with a Pepper robot for Human-Robot Interaction (HRI) in dialogue, as well as an investigation of the effects of additional visual information on response time in different models. The results show that incorporating visual information adds context to the dialogue with only a moderate increase in response time, enabling both the robot and the human to take into account unspoken elements of the situation. Furthermore, using an LLM hosted in Europe offers a solution that complies with European data protection regulations and can therefore facilitate real-life applications more easily.