# CrowdMove: Autonomous Mapless Navigation in Crowded Scenarios

arXiv: https://arxiv.org/abs/1807.07870 · HTML: https://mlsanity.com/paper/1807.07870/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2018.

## Built on by (most cited first, 8 of 8)

- [Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning](https://mlsanity.com/paper/2009.14509/) (2020)
- [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)
- [XAI-N: Sensor-based Robot Navigation using Expert Policies and Decision Trees](https://mlsanity.com/paper/2104.10818/) (2021)
- [Disentangling Uncertainty for Safe Social Navigation using Deep Reinforcement Learning](https://mlsanity.com/paper/2409.10655/) (2024)
- [Shared Control of Holonomic Wheelchairs through Reinforcement Learning](https://mlsanity.com/paper/2507.17055/) (2025)
- [Hierarchical Decision-Making for Autonomous Navigation: Integrating Deep Reinforcement Learning and Fuzzy Logic in Four-Wheel Independent Steering and Driving Systems](https://mlsanity.com/paper/2508.16574/) (2025)
- [Path-conditioned Reinforcement Learning-based Local Planning for Long-Range Navigation](https://mlsanity.com/paper/2603.13888/) (2026)

## Abstract (arXiv)

Navigation is an essential capability for mobile robots. In this paper, we propose a generalized yet effective 3M (i.e., multi-robot, multi-scenario, and multi-stage) training framework. We optimize a mapless navigation policy with a robust policy gradient algorithm. Our method enables different types of mobile platforms to navigate safely in complex and highly dynamic environments, such as pedestrian crowds. To demonstrate the superiority of our method, we test our methods with four kinds of mobile platforms in four scenarios. Videos are available at https://sites.google.com/view/crowdmove.