# Learning to Socially Navigate in Pedestrian-rich Environments with Interaction Capacity

arXiv: https://arxiv.org/abs/2203.16154 · HTML: https://mlsanity.com/paper/2203.16154/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2022.

## Built on by (most cited first, 7 of 7)

- [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)
- [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)
- [Multi-robot Social-aware Cooperative Planning in Pedestrian Environments Using Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2211.15901/) (2022)
- [RUMOR: Reinforcement learning for Understanding a Model of the Real World for Navigation in Dynamic Environments](https://mlsanity.com/paper/2404.16672/) (2024)
- [Towards Socially Compliant Navigation in Deep Reinforcement Learning via Proxemics-Based Reward Modeling](https://mlsanity.com/paper/2608.12917/) (2026)

## Abstract (arXiv)

Existing navigation policies for autonomous robots tend to focus on collision avoidance while ignoring human-robot interactions in social life. For instance, robots can pass along the corridor safer and easier if pedestrians notice them. Sounds have been considered as an efficient way to attract the attention of pedestrians, which can alleviate the freezing robot problem. In this work, we present a new deep reinforcement learning (DRL) based social navigation approach for autonomous robots to move in pedestrian-rich environments with interaction capacity. Most existing DRL based methods intend to train a general policy that outputs both navigation actions, i.e., expected robot's linear and angular velocities, and interaction actions, i.e., the beep action, in the context of reinforcement learning. Different from these methods, we intend to train the policy via both supervised learning and reinforcement learning. In specific, we first train an interaction policy in the context of supervised learning, which provides a better understanding of the social situation, then we use this interaction policy to train the navigation policy via multiple reinforcement learning algorithms. We evaluate our approach in various simulation environments and compare it to other methods. The experimental results show that our approach outperforms others in terms of the success rate. We also deploy the trained policy on a real-world robot, which shows a nice performance in crowded environments.