# Relational Graph Learning for Crowd Navigation

arXiv: https://arxiv.org/abs/1909.13165 · HTML: https://mlsanity.com/paper/1909.13165/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2019.

## Built on by (most cited first, 30 of 48)

- [Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph](https://mlsanity.com/paper/2203.01821/) (2022)
- [Joint Metrics Matter: A Better Standard for Trajectory Forecasting](https://mlsanity.com/paper/2305.06292/) (2023)
- [Interaction Modeling with Multiplex Attention](https://mlsanity.com/paper/2208.10660/) (2022)
- [Important Object Identification with Semi-Supervised Learning for Autonomous Driving](https://mlsanity.com/paper/2203.02634/) (2022)
- [NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning](https://mlsanity.com/paper/2403.06828/) (2024)
- [SICNav-Diffusion: Safe and Interactive Crowd Navigation with Diffusion Trajectory Predictions](https://mlsanity.com/paper/2503.08858/) (2025)
- [Occlusion-Aware Crowd Navigation Using People as Sensors](https://mlsanity.com/paper/2210.00552/) (2022)
- [MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts](https://mlsanity.com/paper/2403.06041/) (2024)
- [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)
- [Robot Navigation in a Crowd by Integrating Deep Reinforcement Learning and Online Planning](https://mlsanity.com/paper/2102.13265/) (2021)
- [Uncertainty-Aware DRL for Autonomous Vehicle Crowd Navigation in Shared Space](https://mlsanity.com/paper/2405.13969/) (2024)
- [Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based Navigation](https://mlsanity.com/paper/2109.11288/) (2021)
- [Socially Integrated Navigation: A Social Acting Robot with Deep Reinforcement Learning](https://mlsanity.com/paper/2403.09793/) (2024)
- [DMMGAN: Diverse Multi Motion Prediction of 3D Human Joints using Attention-Based Generative Adverserial Network](https://mlsanity.com/paper/2209.09124/) (2022)
- [Attention Graph for Multi-Robot Social Navigation with Deep Reinforcement Learning](https://mlsanity.com/paper/2401.17914/) (2024)
- [Decentralized Multi-Robot Navigation for Autonomous Surface Vehicles with Distributional Reinforcement Learning](https://mlsanity.com/paper/2402.11799/) (2024)
- [A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation](https://mlsanity.com/paper/2102.08863/) (2021)
- [Optimal Solving of Constrained Path-Planning Problems with Graph Convolutional Networks and Optimized Tree Search](https://mlsanity.com/paper/2108.01036/) (2021)
- [SocNavGym: A Reinforcement Learning Gym for Social Navigation](https://mlsanity.com/paper/2304.14102/) (2023)
- [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)
- [Exploring Social Motion Latent Space and Human Awareness for Effective Robot Navigation in Crowded Environments](https://mlsanity.com/paper/2310.07335/) (2023)
- [Online Context Learning for Socially Compliant Navigation](https://mlsanity.com/paper/2406.11495/) (2024)
- [Stranger Danger! Identifying and Avoiding Unpredictable Pedestrians in RL-based Social Robot Navigation](https://mlsanity.com/paper/2407.06056/) (2024)
- [Context-aware Mamba-based Reinforcement Learning for social robot navigation](https://mlsanity.com/paper/2408.02661/) (2024)
- [Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches](https://mlsanity.com/paper/2512.00049/) (2025)
- [Crowd-FM: Learned Optimal Selection of Conditional Flow Matching-generated Trajectories for Crowd Navigation](https://mlsanity.com/paper/2602.06698/) (2026)
- [An advantage actor-critic algorithm for robotic motion planning in dense and dynamic scenarios](https://mlsanity.com/paper/2102.03138/) (2021)
- [Multi-subgoal Robot Navigation in Crowds with History Information and Interactions](https://mlsanity.com/paper/2205.02003/) (2022)

## Abstract (arXiv)

We present a relational graph learning approach for robotic crowd navigation using model-based deep reinforcement learning that plans actions by looking into the future. Our approach reasons about the relations between all agents based on their latent features and uses a Graph Convolutional Network to encode higher-order interactions in each agent's state representation, which is subsequently leveraged for state prediction and value estimation. The ability to predict human motion allows us to perform multi-step lookahead planning, taking into account the temporal evolution of human crowds. We evaluate our approach against a state-of-the-art baseline for crowd navigation and ablations of our model to demonstrate that navigation with our approach is more efficient, results in fewer collisions, and avoids failure cases involving oscillatory and freezing behaviors.