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

arXiv: https://arxiv.org/abs/2409.13573 · HTML: https://mlsanity.com/paper/2409.13573/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2024.

## Built on by (most cited first, 2 of 2)

- [Multi-Agent LLM Actor-Critic Framework for Social Robot Navigation](https://mlsanity.com/paper/2503.09758/) (2025)
- [Logic-Guided Socially-aware Robot Navigation World Model](https://mlsanity.com/paper/2510.23509/) (2025)

## Abstract (arXiv)

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.