论文标题
RL星平台：基于模拟的机器人培训的增强学习
RL STaR Platform: Reinforcement Learning for Simulation based Training of Robots
论文作者
论文摘要
强化学习（RL）是一个有前途的领域，可以增强机器人自主权和对太空机器人技术的决策能力，这是由于环境内的随机性和不确定性而在传统技术中具有挑战性的。 RL可用于使用不频繁的人类反馈，更快，更安全的月球表面运动或多机器人系统的协调和协作来实现月球洞穴探索。但是，有许多障碍使研究对使用RL和机器学习的空间机器人应用具有挑战性，尤其是由于Coppeliasim等传统机器人模拟器的资源不足。我们对此的解决方案是一个开源模块化平台，称为“加强学习”，用于基于仿真的机器人或RL星，有助于简化和加速RL在太空机器人研究领域中的应用。本文介绍了RL Star平台，以及研究人员如何通过演示使用它。
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.
