# Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans

arXiv: https://arxiv.org/abs/1911.03074 · HTML: https://mlsanity.com/paper/1911.03074/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2019.

## Built on by (most cited first, 10 of 10)

- [Conflict Avoidance in Social Navigation -- a Survey](https://mlsanity.com/paper/2106.12113/) (2021)
- [HDPlanner: Advancing Autonomous Deployments in Unknown Environments through Hierarchical Decision Networks](https://mlsanity.com/paper/2408.03768/) (2024)
- [Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches](https://mlsanity.com/paper/2512.00049/) (2025)
- [Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation](https://mlsanity.com/paper/2111.06742/) (2021)
- [MeSA-DRL: Memory-Enhanced Deep Reinforcement Learning for Advanced Socially Aware Robot Navigation in Crowded Environments](https://mlsanity.com/paper/2404.05203/) (2024)
- [EmoBipedNav: Emotion-aware Social Navigation for Bipedal Robots with Deep Reinforcement Learning](https://mlsanity.com/paper/2503.12538/) (2025)
- [TRANS: Terrain-aware Reinforcement Learning for Agile Navigation of Quadruped Robots under Social Interactions](https://mlsanity.com/paper/2602.12724/) (2026)
- [Deep Reinforcement Learning-Based Mapless Crowd Navigation with Perceived Risk of the Moving Crowd for Mobile Robots](https://mlsanity.com/paper/2304.03593/) (2023)
- [Improving the Generalization of Unseen Crowd Behaviors for Reinforcement Learning based Local Motion Planners](https://mlsanity.com/paper/2410.12232/) (2024)
- [Don't Just Search, Understand: Semantic Path Planning Agent for Spherical Tensegrity Robots in Unknown Environments](https://mlsanity.com/paper/2511.01236/) (2025)

## Abstract (arXiv)

We propose a method to tackle the problem of mapless collision-avoidance navigation where humans are present using 2D laser scans. Our proposed method uses ego-safety to measure collision from the robot's perspective while social-safety to measure the impact of our robot's actions on surrounding pedestrians. Specifically, the social-safety part predicts the intrusion impact of our robot's action into the interaction area with surrounding humans. We train the policy using reinforcement learning on a simple simulator and directly evaluate the learned policy in Gazebo and real robot tests. Experiments show the learned policy can be smoothly transferred without any fine tuning. We observe that our method demonstrates time-efficient path planning behavior with high success rate in mapless navigation tasks. Furthermore, we test our method in a navigation among dynamic crowds task considering both low and high volume traffic. Our learned policy demonstrates cooperative behavior that actively drives our robot into traffic flows while showing respect to nearby pedestrians. Evaluation videos are at https://sites.google.com/view/ssw-batman