Success stories of applied machine learning can be traced back to the datasets and environments that were put forward as challenges for the community. The challenge that the community sets as a benchmark is usually the challenge that the community eventually solves. The ultimate challenge of reinforcement learning research is to train real agents to operate in the real environment, but until now there has not been a common real-world RL benchmark. In this work, we present a prototype real-world environment from OffWorld Gym -- a collection of real-world environments for reinforcement learning in robotics with free public remote access. Close integration into existing ecosystem allows the community to start using OffWorld Gym without any prior experience in robotics and takes away the burden of managing a physical robotics system, abstracting it under a familiar API. We introduce a navigation task, where a robot has to reach a visual beacon on an uneven terrain using only the camera input and provide baseline results in both the real environment and the simulated replica. To start training, visit https://gym.offworld.ai

中文翻译：

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OffWorld Gym：用于现实世界强化学习基准和研究的开放式物理机器人环境

应用机器学习的成功案例可以追溯到作为社区挑战提出的数据集和环境。社区设定为基准的挑战通常是社区最终解决的挑战。强化学习研究的最终挑战是训练真实的智能体在真实环境中运行，但直到现在还没有一个通用的真实世界 RL 基准。在这项工作中，我们展示了一个来自 OffWorld Gym 的原型真实世界环境——一组用于机器人强化学习的真实世界环境集合，可免费公共远程访问。与现有生态系统的紧密集成使社区无需任何机器人技术经验即可开始使用 OffWorld Gym，并减轻管理物理机器人系统的负担，在熟悉的 API 下抽象它。我们引入了导航任务，其中机器人必须仅使用相机输入到达不平坦地形上的视觉信标，并在真实环境和模拟副本中提供基线结果。要开始训练，请访问 https://gym.offworld.ai