# NaviSTAR: Socially Aware Robot Navigation with Hybrid Spatio-Temporal Graph Transformer and Preference Learning

arXiv: https://arxiv.org/abs/2304.05979 · HTML: https://mlsanity.com/paper/2304.05979/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2023.

## Built on by (most cited first, 13 of 13)

- [DR-MPC: Deep Residual Model Predictive Control for Real-world Social Navigation](https://mlsanity.com/paper/2410.10646/) (2024)
- [HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environments](https://mlsanity.com/paper/2411.12150/) (2024)
- [Multi-Agent LLM Actor-Critic Framework for Social Robot Navigation](https://mlsanity.com/paper/2503.09758/) (2025)
- [Unifying Large Language Model and Deep Reinforcement Learning for Human-in-Loop Interactive Socially-aware Navigation](https://mlsanity.com/paper/2403.15648/) (2024)
- [Ratatouille: Imitation Learning Ingredients for Real-world Social Robot Navigation](https://mlsanity.com/paper/2509.17204/) (2025)
- [Hyper-STTN: Hypergraph Augmented Spatial-Temporal Transformer Network for Trajectory Prediction](https://mlsanity.com/paper/2401.06344/) (2024)
- [Logic-Guided Socially-aware Robot Navigation World Model](https://mlsanity.com/paper/2510.23509/) (2025)
- [Robot Crowd Navigation in Dynamic Environment with Offline Reinforcement Learning](https://mlsanity.com/paper/2312.11032/) (2023)
- [SPLC: Social Preference Learning for Crowd Robot Navigation](https://mlsanity.com/paper/2607.01925/) (2026)
- [HumAIN: Human-Aware Implicit Social Robot Navigation](https://mlsanity.com/paper/2607.07357/) (2026)
- [ACME: A Multi-Cultural, Multi-Embodiment Social-Navigation Dataset](https://mlsanity.com/paper/2607.21964/) (2026)
- [Towards Socially Compliant Navigation in Deep Reinforcement Learning via Proxemics-Based Reward Modeling](https://mlsanity.com/paper/2608.12917/) (2026)
- [SoGuDiff: Socially Guided Diffusion for Steerable, Norm-Grounded Robot Navigation](https://mlsanity.com/paper/2609.30560/) (2026)

## Abstract (arXiv)

Developing robotic technologies for use in human society requires ensuring the safety of robots' navigation behaviors while adhering to pedestrians' expectations and social norms. However, maintaining real-time communication between robots and pedestrians to avoid collisions can be challenging. To address these challenges, we propose a novel socially-aware navigation benchmark called NaviSTAR, which utilizes a hybrid Spatio-Temporal grAph tRansformer (STAR) to understand interactions in human-rich environments fusing potential crowd multi-modal information. We leverage off-policy reinforcement learning algorithm with preference learning to train a policy and a reward function network with supervisor guidance. Additionally, we design a social score function to evaluate the overall performance of social navigation. To compare, we train and test our algorithm and other state-of-the-art methods in both simulator and real-world scenarios independently. Our results show that NaviSTAR outperforms previous methods with outstanding performancefootnoteThe source code and experiment videos of this work are available at: https://sites.google.com/view/san-navistar