We present BricksRL, a platform designed to democratize access to robotics for reinforcement learning research and education. BricksRL facilitates the creation, design, and training of custom LEGO robots in the real world by interfacing them with the TorchRL library for reinforcement learning agents. The integration of TorchRL with the LEGO hubs, via Bluetooth bidirectional communication, enables state-of-the-art reinforcement learning training on GPUs for a wide variety of LEGO builds. This offers a flexible and cost-efficient approach for scaling and also provides a robust infrastructure for robot-environment-algorithm communication. We present various experiments across tasks and robot configurations, providing built plans and training results. Furthermore, we demonstrate that inexpensive LEGO robots can be trained end-to-end in the real world to achieve simple tasks, with training times typically under 120 minutes on a normal laptop. Moreover, we show how users can extend the capabilities, exemplified by the successful integration of non-LEGO sensors. By enhancing accessibility to both robotics and reinforcement learning, BricksRL establishes a strong foundation for democratized robotic learning in research and educational settings.

翻译：本文介绍BricksRL平台，该平台旨在为强化学习研究和教育提供民主化的机器人技术接入途径。BricksRL通过将实体乐高机器人与TorchRL强化学习智能体库对接，支持真实世界中定制化乐高机器人的创建、设计与训练。TorchRL与乐高控制中心通过蓝牙双向通信实现集成，使得各类乐高构建体都能在GPU上进行前沿强化学习训练。这为规模化应用提供了灵活且经济高效的方法，同时为机器人-环境-算法通信建立了稳健的基础架构。我们展示了跨任务与机器人配置的多种实验，并提供构建方案与训练结果。此外，我们验证了低成本乐高机器人能够在真实世界中通过端到端训练完成简单任务，在普通笔记本电脑上训练时间通常低于120分钟。更进一步，我们通过成功集成非乐高传感器的案例，展示了用户如何扩展系统能力。通过提升机器人技术与强化学习的可及性，BricksRL为研究及教育场景下的民主化机器人学习奠定了坚实基础。