# Robot Navigation in Crowds by Graph Convolutional Networks with Attention Learned from Human Gaze

arXiv: https://arxiv.org/abs/1909.10400 · HTML: https://mlsanity.com/paper/1909.10400/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2019.

## Built on by (most cited first, 30 of 30)

- [Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph](https://mlsanity.com/paper/2203.01821/) (2022)
- [Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning](https://mlsanity.com/paper/2009.14509/) (2020)
- [Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Tasks](https://mlsanity.com/paper/2107.05825/) (2021)
- [DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks](https://mlsanity.com/paper/2108.05030/) (2021)
- [AVGCN: Trajectory Prediction using Graph Convolutional Networks Guided by Human Attention](https://mlsanity.com/paper/2101.05682/) (2021)
- [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)
- [Selective Eye-gaze Augmentation To Enhance Imitation Learning In Atari Games](https://mlsanity.com/paper/2012.03145/) (2020)
- [Robot Navigation in a Crowd by Integrating Deep Reinforcement Learning and Online Planning](https://mlsanity.com/paper/2102.13265/) (2021)
- [TBD Pedestrian Data Collection: Towards Rich, Portable, and Large-Scale Natural Pedestrian Data](https://mlsanity.com/paper/2309.17187/) (2023)
- [Socially Integrated Navigation: A Social Acting Robot with Deep Reinforcement Learning](https://mlsanity.com/paper/2403.09793/) (2024)
- [Attention Graph for Multi-Robot Social Navigation with Deep Reinforcement Learning](https://mlsanity.com/paper/2401.17914/) (2024)
- [Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars](https://mlsanity.com/paper/2102.00675/) (2021)
- [A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation](https://mlsanity.com/paper/2102.08863/) (2021)
- [HeR-DRL:Heterogeneous Relational Deep Reinforcement Learning for Decentralized Multi-Robot Crowd Navigation](https://mlsanity.com/paper/2403.10083/) (2024)
- [COLSON: Controllable Learning-Based Social Navigation via Diffusion-Based Reinforcement Learning](https://mlsanity.com/paper/2503.13934/) (2025)
- [Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches](https://mlsanity.com/paper/2512.00049/) (2025)
- [Multi-subgoal Robot Navigation in Crowds with History Information and Interactions](https://mlsanity.com/paper/2205.02003/) (2022)
- [Dense Crowd Flow-Informed Path Planning](https://mlsanity.com/paper/2206.00705/) (2022)
- [Active Classification of Moving Targets with Learned Control Policies](https://mlsanity.com/paper/2212.03068/) (2022)
- [SOCRATES: Text-based Human Search and Approach using a Robot Dog](https://mlsanity.com/paper/2302.05324/) (2023)
- [InCrowdFormer: On-Ground Pedestrian World Model From Egocentric Views](https://mlsanity.com/paper/2303.09534/) (2023)
- [MeSA-DRL: Memory-Enhanced Deep Reinforcement Learning for Advanced Socially Aware Robot Navigation in Crowded Environments](https://mlsanity.com/paper/2404.05203/) (2024)
- [Incremental Residual Reinforcement Learning Toward Real-World Learning for Social Navigation](https://mlsanity.com/paper/2604.07945/) (2026)
- [Relative velocity-based reward functions for crowd navigation of robots](https://mlsanity.com/paper/2112.13984/) (2021)
- [Towards Rich, Portable, and Large-Scale Pedestrian Data Collection](https://mlsanity.com/paper/2203.01974/) (2022)
- [Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle](https://mlsanity.com/paper/2309.06313/) (2023)
- [Integrating Offline Pre-Training with Online Fine-Tuning: A Reinforcement Learning Approach for Robot Social Navigation](https://mlsanity.com/paper/2510.00466/) (2025)
- [Learning-Based Motion Planning for Dynamic Environments: From Foundational Algorithms to Emerging Paradigms](https://mlsanity.com/paper/2608.00625/) (2026)
- [Performance-Preserving Online Adaptation in Social Navigation via Diffusion Steering](https://mlsanity.com/paper/2609.24317/) (2026)

## Abstract (arXiv)

Safe and efficient crowd navigation for mobile robot is a crucial yet challenging task. Previous work has shown the power of deep reinforcement learning frameworks to train efficient policies. However, their performance deteriorates when the crowd size grows. We suggest that this can be addressed by enabling the network to identify and pay attention to the humans in the crowd that are most critical to navigation. We propose a novel network utilizing a graph representation to learn the policy. We first train a graph convolutional network based on human gaze data that accurately predicts human attention to different agents in the crowd. Then we incorporate the learned attention into a graph-based reinforcement learning architecture. The proposed attention mechanism enables the assignment of meaningful weightings to the neighbors of the robot, and has the additional benefit of interpretability. Experiments on real-world dense pedestrian datasets with various crowd sizes demonstrate that our model outperforms state-of-art methods by 18.4% in task accomplishment and by 16.4% in time efficiency.