# RANS: Highly-Parallelised Simulator for Reinforcement Learning based Autonomous Navigating Spacecrafts

arXiv: https://arxiv.org/abs/2310.07393 · HTML: https://mlsanity.com/paper/2310.07393/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2023.

## Built on by (most cited first, 6 of 6)

- [A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation](https://mlsanity.com/paper/2407.08263/) (2024)
- [Space Robotics Bench: Robot Learning Beyond Earth](https://mlsanity.com/paper/2509.23328/) (2025)
- [RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation](https://mlsanity.com/paper/2505.14526/) (2025)
- [Sim2Dust: Mastering Dynamic Waypoint Tracking on Granular Media](https://mlsanity.com/paper/2508.11503/) (2025)
- [Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests](https://mlsanity.com/paper/2505.10033/) (2025)
- [Crossing the Sim2Real Gap Between Simulation and Ground Testing to Space Deployment of Autonomous Free-flyer Control](https://mlsanity.com/paper/2512.03736/) (2025)

## Abstract (arXiv)

Nowadays, realistic simulation environments are essential to validate and build reliable robotic solutions. This is particularly true when using Reinforcement Learning (RL) based control policies. To this end, both robotics and RL developers need tools and workflows to create physically accurate simulations and synthetic datasets. Gazebo, MuJoCo, Webots, Pybullets or Isaac Sym are some of the many tools available to simulate robotic systems. Developing learning-based methods for space navigation is, due to the highly complex nature of the problem, an intensive data-driven process that requires highly parallelized simulations. When it comes to the control of spacecrafts, there is no easy to use simulation library designed for RL. We address this gap by harnessing the capabilities of NVIDIA Isaac Gym, where both physics simulation and the policy training reside on GPU. Building on this tool, we provide an open-source library enabling users to simulate thousands of parallel spacecrafts, that learn a set of maneuvering tasks, such as position, attitude, and velocity control. These tasks enable to validate complex space scenarios, such as trajectory optimization for landing, docking, rendezvous and more.