# Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based Navigation

arXiv: https://arxiv.org/abs/2109.11288 · HTML: https://mlsanity.com/paper/2109.11288/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 23 Sep 2021.

## Builds on (most cited first, 13 of 13)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Socially Aware Motion Planning with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.08862/) (2017)
- [Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning](https://mlsanity.com/paper/1805.01956/) (2018)
- [Learning Navigation Behaviors End-to-End with AutoRL](https://mlsanity.com/paper/1809.10124/) (2018)
- [PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning](https://mlsanity.com/paper/1710.03937/) (2017)
- [Relational Graph Learning for Crowd Navigation](https://mlsanity.com/paper/1909.13165/) (2019)
- [Long-Range Indoor Navigation with PRM-RL](https://mlsanity.com/paper/1902.09458/) (2019)
- [DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs](https://mlsanity.com/paper/1909.13003/) (2019)
- [NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments](https://mlsanity.com/paper/2012.04406/) (2020)
- [Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systems](https://mlsanity.com/paper/2104.03616/) (2021)
- [CrowdMove: Autonomous Mapless Navigation in Crowded Scenarios](https://mlsanity.com/paper/1807.07870/) (2018)
- [SocioSense: Robot Navigation Amongst Pedestrians with Social and Psychological Constraints](https://mlsanity.com/paper/1706.01102/) (2017)

## Built on by (most cited first, 5 of 5)

- [LLM-Driven Robots Risk Enacting Discrimination, Violence, and Unlawful Actions](https://mlsanity.com/paper/2406.08824/) (2024)
- [Semantically-driven Deep Reinforcement Learning for Inspection Path Planning](https://mlsanity.com/paper/2505.14443/) (2025)
- [Stranger Danger! Identifying and Avoiding Unpredictable Pedestrians in RL-based Social Robot Navigation](https://mlsanity.com/paper/2407.06056/) (2024)
- [Disentangling Uncertainty for Safe Social Navigation using Deep Reinforcement Learning](https://mlsanity.com/paper/2409.10655/) (2024)
- [HabitatDyn Dataset: Dynamic Object Detection to Kinematics Estimation](https://mlsanity.com/paper/2304.10854/) (2023)

## Abstract (arXiv)

Intelligent navigation among social crowds is an essential aspect of mobile robotics for applications such as delivery, health care, or assistance. Deep Reinforcement Learning emerged as an alternative planning method to conservative approaches and promises more efficient and flexible navigation. However, in highly dynamic environments employing different kinds of obstacle classes, safe navigation still presents a grand challenge. In this paper, we propose a semantic Deep-reinforcement-learning-based navigation approach that teaches object-specific safety rules by considering high-level obstacle information. In particular, the agent learns object-specific behavior by contemplating the specific danger zones to enhance safety around vulnerable object classes. We tested the approach against a benchmark obstacle avoidance approach and found an increase in safety. Furthermore, we demonstrate that the agent could learn to navigate more safely by keeping an individual safety distance dependent on the semantic information.