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

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.