A Lightweight Crowd Model for Robot Social Navigation

TL;DR

This paper introduces a lightweight, real-time macroscopic crowd prediction model that improves accuracy and efficiency, enabling robots to navigate dense human environments socially compliantly.

Contribution

A novel simplified macroscopic crowd model that balances prediction accuracy with computational efficiency for robot navigation.

Findings

3.6 times reduction in inference time

3.1% improvement in prediction accuracy

Enables socially compliant robot navigation in real-time

Abstract

Robots operating in human-populated environments must navigate safely and efficiently while minimizing social disruption. Achieving this requires estimating crowd movement to avoid congested areas in real-time. Traditional microscopic models struggle to scale in dense crowds due to high computational cost, while existing macroscopic crowd prediction models tend to be either overly simplistic or computationally intensive. In this work, we propose a lightweight, real-time macroscopic crowd prediction model tailored for human motion, which balances prediction accuracy and computational efficiency. Our approach simplifies both spatial and temporal processing based on the inherent characteristics of pedestrian flow, enabling robust generalization without the overhead of complex architectures. We demonstrate a 3.6 times reduction in inference time, while improving prediction accuracy by 3.1 %. Integrated into a socially aware planning framework, the model enables efficient and socially compliant robot navigation in dynamic environments. This work highlights that efficient human crowd modeling enables robots to navigate dense environments without costly computations.