DRL-SFM: Learning Social Navigation from Costmaps and Social Forces for Mobile Robots and Intelligent Wheelchairs

Abstract

The demand for assistive robots for passenger transport, such as intelligent wheelchairs, is increasing rapidly due to demographic changes. To allow passengers to navigate in crowded environments, such as shopping malls and hospitals, these systems must navigate in a socially accepted manner that ensures the comfort of both passengers and surrounding pedestrians. Although deep reinforcement learning (DRL) has shown promising results for social navigation, existing planners often learn overly passive behaviors, not engaging in the mutual adaptation characteristic of human interaction. In this paper, we introduce a novel DRL-based local planner that learns navigation behaviors by integrating the Social Force Model (SFM) directly into its reward function, allowing more cooperative interactions for mobile robots and intelligent wheelchairs. This approach encourages the agent to learn more forward-looking and mutual navigation policies by rewarding actions that align with the dynamics of pedestrians. To ensure generalization and straightforward deployment, our method utilizes the standard Navigation 2 local costmap augmented with pedestrian detections as an observation. The experiments demonstrate that our agent achieves a higher success rate in crowded scenarios with fewer space intrusions, outperforming the state-of-the-art DRL planner based on velocity obstacles by up to 11%.