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.

翻译：摘要：如今，逼真的仿真环境对于验证和构建可靠的机器人解决方案至关重要，尤其是在使用基于强化学习的控制策略时。为此，机器人与强化学习开发者需要合适的工具和工作流程来创建物理精确的仿真环境及合成数据集。Gazebo、MuJoCo、Webots、Pybullet 与 Isaac Sim 等众多工具均可用于机器人系统仿真。由于太空导航问题的高度复杂性，基于学习方法的发展需要依赖密集的数据驱动过程，这要求高度并行的仿真能力。然而在航天器控制领域，目前尚无专为强化学习设计的易用仿真库。我们通过利用 NVIDIA Isaac Gym 的能力弥补了这一空白，该平台可将物理仿真与策略训练同时部署于 GPU 上。基于此工具，我们提供了一款开源库，使用户能够模拟数千个并行运行的航天器，学习位置、姿态和速度控制等机动任务。这些任务可验证复杂的太空场景，如着陆、对接、交会等轨迹优化问题。