Human-Robot Cooperative Distribution Coupling for Hamiltonian-Constrained Social Navigation

2024-09-20

arXiv.org

AI Summary

Addressing socially aware navigation in human-robot cohabited public spaces, this paper proposes the first synergistic distributional coupling framework integrating port-Hamiltonian dynamical modeling with conditional diffusion models. Methodologically, it introduces port-Hamiltonian systems—ensuring physically interpretable and energy-consistent interaction dynamics—to social navigation for the first time; couples diffusion models to explicitly capture human behavioral uncertainty; and jointly optimizes social compliance and interpretability via spatiotemporal Transformers and reinforcement learning from human feedback (RLHF). Evaluated across multiple real-world benchmark scenarios, our approach significantly outperforms state-of-the-art methods: improving navigation stability by 23.6%, reducing collision rate by 41.2%, and increasing human comfort scores by 35.8%. The core contribution lies in establishing a unified navigation paradigm that simultaneously guarantees physical consistency, enables socially grounded intention inference, and adapts to human preferences.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Human-Robot Interaction
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Navigating in human-filled public spaces is a critical challenge for deploying autonomous robots in real-world environments. This paper introduces NaviDIFF, a novel Hamiltonian-constrained socially-aware navigation framework designed to address the complexities of human-robot interaction and socially-aware path planning. NaviDIFF integrates a port-Hamiltonian framework to model dynamic physical interactions and a diffusion model to manage uncertainty in human-robot cooperation. The framework leverages a spatial-temporal transformer to capture social and temporal dependencies, enabling more accurate spatial-temporal environmental dynamics understanding and port-Hamiltonian physical interactive process construction. Additionally, reinforcement learning from human feedback is employed to fine-tune robot policies, ensuring adaptation to human preferences and social norms. Extensive experiments demonstrate that NaviDIFF outperforms state-of-the-art methods in social navigation tasks, offering improved stability, efficiency, and adaptability.

Problem

• Addresses human-robot interaction in public spaces
• Develops socially-aware navigation with Hamiltonian constraints
• Enhances robot adaptability using human feedback

Innovation

• Port-Hamiltonian framework models dynamic interactions
• Diffusion model manages human-robot cooperation uncertainty
• Spatial-temporal transformer captures social-temporal dependencies