# Multi-Robot Cooperative Socially-Aware Navigation Using Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2309.15234 · HTML: https://mlsanity.com/paper/2309.15234/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2023.

## Built on by (most cited first, 17 of 17)

- [From Cognition to Precognition: A Future-Aware Framework for Social Navigation](https://mlsanity.com/paper/2409.13244/) (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)
- [HeR-DRL:Heterogeneous Relational Deep Reinforcement Learning for Decentralized Multi-Robot Crowd Navigation](https://mlsanity.com/paper/2403.10083/) (2024)
- [GlobeDiff: State Diffusion Process for Partial Observability in Multi-Agent Systems](https://mlsanity.com/paper/2602.15776/) (2026)
- [High-order Interactions Modeling for Interpretable Multi-Agent Q-Learning](https://mlsanity.com/paper/2510.20218/) (2025)
- [Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches](https://mlsanity.com/paper/2512.00049/) (2025)
- [Hyper-STTN: Hypergraph Augmented Spatial-Temporal Transformer Network for Trajectory Prediction](https://mlsanity.com/paper/2401.06344/) (2024)
- [HARP: Human-Assisted Regrouping with Permutation Invariant Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2409.11741/) (2024)
- [Policies over Poses: Reinforcement Learning based Distributed Pose-Graph Optimization for Multi-Robot SLAM](https://mlsanity.com/paper/2510.22740/) (2025)
- [Belief Aided Navigation using Bayesian Reinforcement Learning for Avoiding Humans in Blind Spots](https://mlsanity.com/paper/2403.10105/) (2024)
- [ICCO: Learning an Instruction-conditioned Coordinator for Language-guided Task-aligned Multi-robot Control](https://mlsanity.com/paper/2503.12122/) (2025)
- [Learning to Navigate Socially Through Proactive Risk Perception](https://mlsanity.com/paper/2510.07871/) (2025)
- [Intrinsic-Motivation Multi-Robot Social Formation Navigation with Coordinated Exploration](https://mlsanity.com/paper/2512.13293/) (2025)
- [GIANT - Global Path Integration and Attentive Graph Networks for Multi-Agent Trajectory Planning](https://mlsanity.com/paper/2603.04659/) (2026)
- [Cross-Modal Navigation with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2605.06595/) (2026)
- [KinematicRL: A Sim-to-Real Reinforcement Learning Framework For Social Navigation With Kinodynamic Feasibility](https://mlsanity.com/paper/2606.12042/) (2026)

## Abstract (arXiv)

In public spaces shared with humans, ensuring multi-robot systems navigate without collisions while respecting social norms is challenging, particularly with limited communication. Although current robot social navigation techniques leverage advances in reinforcement learning and deep learning, they frequently overlook robot dynamics in simulations, leading to a simulation-to-reality gap. In this paper, we bridge this gap by presenting a new multi-robot social navigation environment crafted using Dec-POSMDP and multi-agent reinforcement learning. Furthermore, we introduce SAMARL: a novel benchmark for cooperative multi-robot social navigation. SAMARL employs a unique spatial-temporal transformer combined with multi-agent reinforcement learning. This approach effectively captures the complex interactions between robots and humans, thus promoting cooperative tendencies in multi-robot systems. Our extensive experiments reveal that SAMARL outperforms existing baseline and ablation models in our designed environment. Demo videos for this work can be found at: https://sites.google.com/view/samarl