论文标题
Realant：一个开源低成本四倍，用于现实世界增强学习的教育和研究
RealAnt: An Open-Source Low-Cost Quadruped for Education and Research in Real-World Reinforcement Learning
论文作者
论文摘要
当前可用于研究的机器人平台要么非常昂贵，要么无法处理强化学习中滥用探索性控制。我们开发了Realant，这是一种流行的“蚂蚁”基准用于增强学习的最小低成本物理版本。 Realant仅需$ \ sim $ 350欧元（\ $ 410）的材料，并且可以在不到一个小时内组装。我们通过增强学习实验验证平台，并在一组基准任务上提供基线结果。我们证明，Realant机器人可以从不到10分钟的经验中学习从头开始走路。我们还提供了Mujoco和Pybullet模拟器中的机器人的模拟器版本（具有相同的维度，状态行动空间和延迟的噪声观测值）。我们开放源硬件设计，支持软件和基线结果，以供教育使用和可再现研究。
Current robot platforms available for research are either very expensive or unable to handle the abuse of exploratory controls in reinforcement learning. We develop RealAnt, a minimal low-cost physical version of the popular `Ant' benchmark used in reinforcement learning. RealAnt costs only $\sim$350 EUR (\$410) in materials and can be assembled in less than an hour. We validate the platform with reinforcement learning experiments and provide baseline results on a set of benchmark tasks. We demonstrate that the RealAnt robot can learn to walk from scratch from less than 10 minutes of experience. We also provide simulator versions of the robot (with the same dimensions, state-action spaces, and delayed noisy observations) in the MuJoCo and PyBullet simulators. We open-source hardware designs, supporting software, and baseline results for educational use and reproducible research.
