GSON: A Group-based Social Navigation Framework with Large Multimodal Model
This addresses the need for socially aware navigation in service robots and autonomous vehicles, representing an incremental improvement with a novel integration of LMMs.
The paper tackles the problem of enabling robots to navigate in human environments with social awareness by introducing GSON, a group-based social navigation framework that uses Large Multimodal Models for zero-shot social relationship extraction. Results show it significantly outperforms existing approaches in minimizing social perturbations while maintaining comparable traditional navigation performance.
With the increasing presence of service robots and autonomous vehicles in human environments, navigation systems need to evolve beyond simple destination reach to incorporate social awareness. This paper introduces GSON, a novel group-based social navigation framework that leverages Large Multimodal Models (LMMs) to enhance robots' social perception capabilities. Our approach uses visual prompting to enable zero-shot extraction of social relationships among pedestrians and integrates these results with robust pedestrian detection and tracking pipelines to overcome the inherent inference speed limitations of LMMs. The planning system incorporates a mid-level planner that sits between global path planning and local motion planning, effectively preserving both global context and reactive responsiveness while avoiding disruption of the predicted social group. We validate GSON through extensive real-world mobile robot navigation experiments involving complex social scenarios such as queuing, conversations, and photo sessions. Comparative results show that our system significantly outperforms existing navigation approaches in minimizing social perturbations while maintaining comparable performance on traditional navigation metrics.