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

Understand

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.