RealAnt：一个开源的低成本四足动物，用于现实世界的强化学习教育和研究
RealAnt: An Open-Source Low-Cost Quadruped for Education and Research in Real-World Reinforcement Learning
摘要：
目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探索性控制的滥用。我们开发了RealAnt，这是强化学习中使用的流行“Ant”基准的最小低成本物理版本。RealAnt的材料成本仅为$\sim$ 350欧元（约合410美元），组装时间不到一小时。我们用强化学习实验验证了该平台，并在一组基准任务上提供了基线结果。我们证明了RealAnt机器人可以从不到10分钟的经验中从头开始学习走路。我们还在MuJoCo和PyBullet模拟器中提供了机器人的模拟器版本（具有相同的尺寸，状态-动作空间和延迟噪声观测）。我们开源硬件设计，支持软件和基线结果用于教育用途和可重复研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
