BricksRL: A Platform for Democratizing Robotics and Reinforcement Learning Research and Education with LEGO

2024-06-25 · Sebastian Dittert, Vincent Moens, Gianni de Fabritiis

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.

📄 PDF Abstract BibTeX arXiv:2406.17490

Code (3)

BricksRL/bricksrl 공식 구현 pytorch

facebookresearch/rl jax

pytorch/rl jax

Tasks

reinforcement-learning Reinforcement Learning

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