Reinforcement learning (RL) is a promising field to enhance robotic autonomy and decision making capabilities for space robotics, something which is challenging with traditional techniques due to stochasticity and uncertainty within the environment. RL can be used to enable lunar cave exploration with infrequent human feedback, faster and safer lunar surface locomotion or the coordination and collaboration of multi-robot systems. However, there are many hurdles making research challenging for space robotic applications using RL and machine learning, particularly due to insufficient resources for traditional robotics simulators like CoppeliaSim. Our solution to this is an open source modular platform called Reinforcement Learning for Simulation based Training of Robots, or RL STaR, that helps to simplify and accelerate the application of RL to the space robotics research field. This paper introduces the RL STaR platform, and how researchers can use it through a demonstration.

中文翻译：

---

RL STAR 平台：强化学习用于基于仿真的机器人训练

强化学习 (RL) 是一个很有前景的领域，可以增强空间机器人的机器人自主性和决策能力，由于环境中的随机性和不确定性，传统技术对这一点提出了挑战。RL 可用于通过不频繁的人类反馈、更快、更安全的月球表面运动或多机器人系统的协调与协作来实现月球洞穴探索。然而，使用 RL 和机器学习的空间机器人应用研究面临许多障碍，特别是由于 CoppeliaSim 等传统机器人模拟器的资源不足。我们的解决方案是一个开源模块化平台，称为强化学习，用于基于仿真的机器人训练，或 RL STaR，这有助于简化和加速 RL 在空间机器人研究领域的应用。本文通过演示介绍了 RL STaR 平台，以及研究人员如何使用它。