# RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation

arXiv: https://arxiv.org/abs/2505.14526 · HTML: https://mlsanity.com/paper/2505.14526/

## 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 24 of its 36 references that are in the map.
- First posted on arXiv 20 May 2025.

## 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, 24 of 24)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [RLBench: The Robot Learning Benchmark & Learning Environment](https://mlsanity.com/paper/1909.12271/) (2019)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [robosuite: A Modular Simulation Framework and Benchmark for Robot Learning](https://mlsanity.com/paper/2009.12293/) (2020)
- [Habitat 2.0: Training Home Assistants to Rearrange their Habitat](https://mlsanity.com/paper/2106.14405/) (2021)
- [Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments](https://mlsanity.com/paper/2301.04195/) (2023)
- [FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation](https://mlsanity.com/paper/2305.12821/) (2023)
- [IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks](https://mlsanity.com/paper/1911.07246/) (2019)
- [skrl: Modular and Flexible Library for Reinforcement Learning](https://mlsanity.com/paper/2202.03825/) (2022)
- [Benchmarking Metric Ground Navigation](https://mlsanity.com/paper/2008.13315/) (2020)
- [Benchmarking Reinforcement Learning Techniques for Autonomous Navigation](https://mlsanity.com/paper/2210.04839/) (2022)
- [Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments](https://mlsanity.com/paper/2005.13857/) (2020)
- [Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning](https://mlsanity.com/paper/2502.19652/) (2025)
- [Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation](https://mlsanity.com/paper/2405.16266/) (2024)
- [RANS: Highly-Parallelised Simulator for Reinforcement Learning based Autonomous Navigating Spacecrafts](https://mlsanity.com/paper/2310.07393/) (2023)
- [A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation](https://mlsanity.com/paper/2407.08263/) (2024)
- [Benchmarking Deep Reinforcement Learning for Navigation in Denied Sensor Environments](https://mlsanity.com/paper/2410.14616/) (2024)
- [A Survey on Reinforcement Learning Applications in SLAM](https://mlsanity.com/paper/2408.14518/) (2024)
- [FlightBench: Benchmarking Learning-based Methods for Ego-vision-based Quadrotors Navigation](https://mlsanity.com/paper/2406.05687/) (2024)
- [Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning](https://mlsanity.com/paper/2405.20534/) (2024)

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

- [Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI](https://mlsanity.com/paper/2510.04978/) (2025)
- [Reinforcement Learning based 6-DoF Maneuvers for Microgravity Intravehicular Docking: A Simulation Study with Int-Ball2 in ISS-JEM](https://mlsanity.com/paper/2512.13514/) (2025)
- [SCoCaT: Success Conditioned Constrained Reinforcement Learning for Spacecraft Docking](https://mlsanity.com/paper/2609.06061/) (2026)

## Abstract (arXiv)

Autonomous robots must navigate and operate in diverse environments, from terrestrial and aquatic settings to aerial and space domains. While Reinforcement Learning (RL) has shown promise in training policies for specific autonomous robots, existing frameworks and benchmarks are often constrained to unique platforms, limiting generalization and fair comparisons across different mobility systems. In this paper, we present a multi-domain framework for training, evaluating and deploying RL-based navigation policies across diverse robotic platforms and operational environments. Our work presents four key contributions: (1) a scalable and modular framework, facilitating seamless robot-task interchangeability and reproducible training pipelines; (2) sim-to-real transfer demonstrated through real-world experiments with multiple robots, including a satellite robotic simulator, an unmanned surface vessel, and a wheeled ground vehicle; (3) the release of the first open-source API for deploying Isaac Lab-trained policies to real robots, enabling lightweight inference and rapid field validation; and (4) uniform tasks and metrics for cross-medium evaluation, through a unified evaluation testbed to assess performance of navigation tasks in diverse operational conditions (aquatic, terrestrial and space). By ensuring consistency between simulation and real-world deployment, RoboRAN lowers the barrier to developing adaptable RL-based navigation strategies. Its modular design enables straightforward integration of new robots and tasks through predefined templates, fostering reproducibility and extension to diverse domains. To support the community, we release RoboRAN as open-source.