# OLiVia-Nav: An Online Lifelong Vision Language Approach for Mobile Robot Social Navigation

arXiv: https://arxiv.org/abs/2409.13675 · HTML: https://mlsanity.com/paper/2409.13675/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2024.

## Built on by (most cited first, 18 of 18)

- [AutoSpatial: Visual-Language Reasoning for Social Robot Navigation through Efficient Spatial Reasoning Learning](https://mlsanity.com/paper/2503.07557/) (2025)
- [LookOut: Real-World Humanoid Egocentric Navigation](https://mlsanity.com/paper/2508.14466/) (2025)
- [GSON: A Group-based Social Navigation Framework with Large Multimodal Model](https://mlsanity.com/paper/2409.18084/) (2024)
- [SocialNav-MoE: A Mixture-of-Experts Vision Language Model for Socially Compliant Navigation with Reinforcement Fine-Tuning](https://mlsanity.com/paper/2512.14757/) (2025)
- [From Obstacles to Etiquette: Robot Social Navigation with VLM-Informed Path Selection](https://mlsanity.com/paper/2602.09002/) (2026)
- [Embodied AI with Foundation Models for Mobile Service Robots: A Systematic Review](https://mlsanity.com/paper/2505.20503/) (2025)
- [Narrate2Nav: Real-Time Visual Navigation with Implicit Language Reasoning in Human-Centric Environments](https://mlsanity.com/paper/2506.14233/) (2025)
- [SplatSearch: Instance Image Goal Navigation for Mobile Robots using 3D Gaussian Splatting and Diffusion Models](https://mlsanity.com/paper/2511.12972/) (2025)
- [Probing Prompt Design for Socially Compliant Robot Navigation with Vision Language Models](https://mlsanity.com/paper/2601.14622/) (2026)
- [Human-Aware Robot Behaviour in Self-Driving Labs](https://mlsanity.com/paper/2603.08420/) (2026)
- [Enhancing Lightweight Vision Language Models through Group Competitive Learning for Socially Compliant Navigation](https://mlsanity.com/paper/2603.11447/) (2026)
- [StateLinFormer: Stateful Training Enhancing Long-term Memory in Navigation](https://mlsanity.com/paper/2603.23571/) (2026)
- [Where Did It Go Wrong? Capability-Oriented Failure Attribution for Vision-and-Language Navigation Agents](https://mlsanity.com/paper/2604.25161/) (2026)
- [HumAIN: Human-Aware Implicit Social Robot Navigation](https://mlsanity.com/paper/2607.07357/) (2026)
- [Think When It Matters: Conditional VLM Reasoning for Social Navigation with RL Policies](https://mlsanity.com/paper/2607.10991/) (2026)
- [ACME: A Multi-Cultural, Multi-Embodiment Social-Navigation Dataset](https://mlsanity.com/paper/2607.21964/) (2026)
- ["Dear LLaVA, Please Drive": A Depth-Aware Vision-Language Agent for Closed-Loop Robotic Control](https://mlsanity.com/paper/2609.22925/) (2026)
- [Where Should I Join? Robot Group Joining via Language-Guided Goal Prediction](https://mlsanity.com/paper/2609.28467/) (2026)

## Abstract (arXiv)

Service robots in human-centered environments such as hospitals, office buildings, and long-term care homes need to navigate while adhering to social norms to ensure the safety and comfortability of the people they are sharing the space with. Furthermore, they need to adapt to new social scenarios that can arise during robot navigation. In this paper, we present a novel Online Lifelong Vision Language architecture, OLiVia- Nav, which uniquely integrates vision-language models (VLMs) with an online lifelong learning framework for robot social navigation. We introduce a unique distillation approach, Social Context Contrastive Language Image Pre-training (SC-CLIP), to transfer the social reasoning capabilities of large VLMs to a lightweight VLM, in order for OLiVia-Nav to directly encode social and environment context during robot navigation. These encoded embeddings are used to generate and select robot social compliant trajectories. The lifelong learning capabilities of SC-CLIP enable OLiVia-Nav to update the robot trajectory planning overtime as new social scenarios are encountered. We conducted extensive real-world experiments in diverse social navigation scenarios. The results showed that OLiVia-Nav outperformed existing state-of-the-art DRL and VLM methods in terms of mean squared error, Hausdorff loss, and personal space violation duration. Ablation studies also verified the design choices for OLiVia-Nav.