# Robot Navigation with Entity-Based Collision Avoidance using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2408.14183 · HTML: https://mlsanity.com/paper/2408.14183/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 26 Aug 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 8 of 8)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [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)
- [Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning](https://mlsanity.com/paper/1609.07845/) (2016)
- [Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning](https://mlsanity.com/paper/1805.01956/) (2018)
- [Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1710.02543/) (2017)
- [Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation](https://mlsanity.com/paper/1609.06838/) (2016)

## Built on by (most cited first, 3 of 3)

- [Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches](https://mlsanity.com/paper/2512.00049/) (2025)
- [Hybrid Motion Planning with Deep Reinforcement Learning for Mobile Robot Navigation](https://mlsanity.com/paper/2512.24651/) (2025)
- [A Convergence Framework for Deep V-Learning: Error Propagation and Sharp Action-Gap Bounds](https://mlsanity.com/paper/2609.18782/) (2026)

## Abstract (arXiv)

Efficient navigation in dynamic environments is crucial for autonomous robots interacting with moving agents and static obstacles. We present a novel deep reinforcement learning approach that improves robot navigation and interaction with different types of agents and obstacles based on specific safety requirements. Our approach uses information about the entity types, improving collision avoidance and ensuring safer navigation. We introduce a new reward function that penalizes the robot for being close to or colliding with different entities such as adults, bicyclists, children, and static obstacles, while also encouraging the robot's progress toward the goal. We propose an optimized algorithm that significantly accelerates the training, validation, and testing phases, enabling efficient learning in complex environments. Comprehensive experiments demonstrate that our approach consistently outperforms state-of-the-art navigation and collision avoidance methods.