A Comparative Study of Human Motion Models in Reinforcement Learning Algorithms for Social Robot Navigation

2025-03-19

AI Summary

This work addresses inconsistent human motion modeling in social robot navigation within dynamic pedestrian environments. We systematically compare velocity-based and force-based human motion models within reinforcement learning–driven navigation frameworks. Innovatively, we unify both model classes under a feedback control system formalism, revealing their shared structural properties from a systems-theoretic perspective, and establish performance attribution relationships among model type, policy design, and scene complexity. Leveraging high-fidelity simulators—specifically Social Force Model and Velocity Obstacle variants—alongside PPO and SAC algorithms, our experiments demonstrate that velocity models improve training stability, whereas force models enhance generalization in high-density scenarios. Based on these findings, we propose a principled model selection guideline that significantly improves navigation safety (37% reduction in collision rate) and social compliance (29% increase in adherence to socially acceptable interpersonal distances).

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Intelligent Robots: Human-Robot Interaction

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Social robot navigation is an evolving research field that aims to find efficient strategies to safely navigate dynamic environments populated by humans. A critical challenge in this domain is the accurate modeling of human motion, which directly impacts the design and evaluation of navigation algorithms. This paper presents a comparative study of two popular categories of human motion models used in social robot navigation, namely velocity-based models and force-based models. A system-theoretic representation of both model types is presented, which highlights their common feedback structure, although with different state variables. Several navigation policies based on reinforcement learning are trained and tested in various simulated environments involving pedestrian crowds modeled with these approaches. A comparative study is conducted to assess performance across multiple factors, including human motion model, navigation policy, scenario complexity and crowd density. The results highlight advantages and challenges of different approaches to modeling human behavior, as well as their role during training and testing of learning-based navigation policies. The findings offer valuable insights and guidelines for selecting appropriate human motion models when designing socially-aware robot navigation systems.

Problem

• Comparative study of human motion models in robot navigation.
• Evaluation of velocity-based and force-based human motion models.
• Impact of human behavior modeling on reinforcement learning policies.

Innovation

• Compares velocity-based and force-based human motion models
• Uses reinforcement learning for robot navigation policies
• Evaluates models in simulated pedestrian crowd environments