# NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments

arXiv: https://arxiv.org/abs/2012.04406 · HTML: https://mlsanity.com/paper/2012.04406/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2020.

## Built on by (most cited first, 15 of 15)

- [Principles and Guidelines for Evaluating Social Robot Navigation Algorithms](https://mlsanity.com/paper/2306.16740/) (2023)
- [Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systems](https://mlsanity.com/paper/2104.03616/) (2021)
- [Reinforcement Learning for Collision-free Flight Exploiting Deep Collision Encoding](https://mlsanity.com/paper/2402.03947/) (2024)
- [NavDreams: Towards Camera-Only RL Navigation Among Humans](https://mlsanity.com/paper/2203.12299/) (2022)
- [Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators](https://mlsanity.com/paper/2104.03663/) (2021)
- [Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based Navigation](https://mlsanity.com/paper/2109.11288/) (2021)
- [Arena 4.0: A Comprehensive ROS2 Development and Benchmarking Platform for Human-centric Navigation Using Generative-Model-based Environment Generation](https://mlsanity.com/paper/2409.12471/) (2024)
- [Arena-Rosnav 2.0: A Development and Benchmarking Platform for Robot Navigation in Highly Dynamic Environments](https://mlsanity.com/paper/2302.10023/) (2023)
- [Holistic Deep-Reinforcement-Learning-based Training of Autonomous Navigation Systems](https://mlsanity.com/paper/2302.02921/) (2023)
- [Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as Observation](https://mlsanity.com/paper/2301.05980/) (2023)
- [Arena-Web -- A Web-based Development and Benchmarking Platform for Autonomous Navigation Approaches](https://mlsanity.com/paper/2302.02898/) (2023)
- [VAPOR: Legged Robot Navigation in Outdoor Vegetation Using Offline Reinforcement Learning](https://mlsanity.com/paper/2309.07832/) (2023)
- [Landmark Guided Active Exploration with State-specific Balance Coefficient](https://mlsanity.com/paper/2306.17484/) (2023)
- [Self-Predictive Representation for Autonomous UAV Object-Goal Navigation](https://mlsanity.com/paper/2604.21130/) (2026)
- [End-to-End Safe Social Navigation via Multi-Task Reinforcement Learning and Probabilistic Perception](https://mlsanity.com/paper/2610.05733/) (2026)

## Abstract (arXiv)

Robot navigation is a task where reinforcement learning approaches are still unable to compete with traditional path planning. State-of-the-art methods differ in small ways, and do not all provide reproducible, openly available implementations. This makes comparing methods a challenge. Recent research has shown that unsupervised learning methods can scale impressively, and be leveraged to solve difficult problems. In this work, we design ways in which unsupervised learning can be used to assist reinforcement learning for robot navigation. We train two end-to-end, and 18 unsupervised-learning-based architectures, and compare them, along with existing approaches, in unseen test cases. We demonstrate our approach working on a real life robot. Our results show that unsupervised learning methods are competitive with end-to-end methods. We also highlight the importance of various components such as input representation, predictive unsupervised learning, and latent features. We make all our models publicly available, as well as training and testing environments, and tools. This release also includes OpenAI-gym-compatible environments designed to emulate the training conditions described by other papers, with as much fidelity as possible. Our hope is that this helps in bringing together the field of RL for robot navigation, and allows meaningful comparisons across state-of-the-art methods.