# Crowd-Aware Robot Navigation for Pedestrians with Multiple Collision Avoidance Strategies via Map-based Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2109.02541 · HTML: https://mlsanity.com/paper/2109.02541/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2021.

## Built on by (most cited first, 12 of 12)

- [LDP: A Local Diffusion Planner for Efficient Robot Navigation and Collision Avoidance](https://mlsanity.com/paper/2407.01950/) (2024)
- [PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learning](https://mlsanity.com/paper/2310.13295/) (2023)
- [Socially Integrated Navigation: A Social Acting Robot with Deep Reinforcement Learning](https://mlsanity.com/paper/2403.09793/) (2024)
- [COLSON: Controllable Learning-Based Social Navigation via Diffusion-Based Reinforcement Learning](https://mlsanity.com/paper/2503.13934/) (2025)
- [Disentangling Uncertainty for Safe Social Navigation using Deep Reinforcement Learning](https://mlsanity.com/paper/2409.10655/) (2024)
- [Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches](https://mlsanity.com/paper/2512.00049/) (2025)
- [Evaluating MEDIRL: A Replication and Ablation Study of Maximum Entropy Deep Inverse Reinforcement Learning for Human Social Navigation](https://mlsanity.com/paper/2406.00968/) (2024)
- [TRANS: Terrain-aware Reinforcement Learning for Agile Navigation of Quadruped Robots under Social Interactions](https://mlsanity.com/paper/2602.12724/) (2026)
- [Deep Reinforcement Learning for Localizability-Enhanced Navigation in Dynamic Human Environments](https://mlsanity.com/paper/2303.12354/) (2023)
- [RUMOR: Reinforcement learning for Understanding a Model of the Real World for Navigation in Dynamic Environments](https://mlsanity.com/paper/2404.16672/) (2024)
- [MAER-Nav: Bidirectional Motion Learning Through Mirror-Augmented Experience Replay for Robot Navigation](https://mlsanity.com/paper/2503.23908/) (2025)
- [Shared Control of Holonomic Wheelchairs through Reinforcement Learning](https://mlsanity.com/paper/2507.17055/) (2025)

## Abstract (arXiv)

It is challenging for a mobile robot to navigate through human crowds. Existing approaches usually assume that pedestrians follow a predefined collision avoidance strategy, like social force model (SFM) or optimal reciprocal collision avoidance (ORCA). However, their performances commonly need to be further improved for practical applications, where pedestrians follow multiple different collision avoidance strategies. In this paper, we propose a map-based deep reinforcement learning approach for crowd-aware robot navigation with various pedestrians. We use the sensor map to represent the environmental information around the robot, including its shape and observable appearances of obstacles. We also introduce the pedestrian map that specifies the movements of pedestrians around the robot. By applying both maps as inputs of the neural network, we show that a navigation policy can be trained to better interact with pedestrians following different collision avoidance strategies. We evaluate our approach under multiple scenarios both in the simulator and on an actual robot. The results show that our approach allows the robot to successfully interact with various pedestrians and outperforms compared methods in terms of the success rate.