利用虚拟现实和强化学习实现微型仿人机器人的远程定位操作
Towards Miniature Humanoid Tele-Loco-Manipulation Using Virtual Reality and Reinforcement Learning
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中文总结 AI 辅助
研究针对微型仿人机器人缺乏类似全尺寸机器人控制堆栈的问题，利用虚拟现实和强化学习开发了控制堆栈，经实验验证能实现一定速度行走及远程定位操作，展现了微型仿人机器人远程定位操作的潜力。
中文摘要 AI 辅助
近年来，全尺寸仿人机器人能力呈指数级增长，旨在在人类环境中进行通用部署。制造商常用的一种控制方法是利用虚拟现实进行上身遥操作，利用强化学习进行下身平衡和运动控制。然而，这种强大的控制堆栈通常只用于昂贵的全尺寸机器人，许多研究团队无法使用。微型仿人机器人更为普遍，但设计中仿生学应用较少，且缺乏类似的发展。本文描述了一种专门为微型仿人机器人从头开发的柔顺全身临场感控制堆栈。在ROBOTIS OP3硬件上进行的框架实验展示了最高可达0.45 m/s的独立于手臂运动的行走速度。通过与专业人类操作员进行的立方体重新定位实验演示了远程定位操作。平均而言，遥控操作系统在10分钟内移动了2个不同的40 g立方体，总共行走了5 m。总体而言，所开发的系统显示出微型仿人机器人远程定位操作的潜力。
英文摘要
Full-sized humanoid robot capabilities have grown exponentially in recent years, aiming towards general-purpose deployment in human environments. A popular control method used by manufacturers utilizes Virtual Reality for upper-body teleoperation and Reinforcement Learning for lower-body balance and locomotion control. As a result, a single remote operator can see, manipulate, and navigate about a real, distant physical environment. This powerful control stack is often relegated to expensive full-sized robots, many of which are inaccessible to the research community. Miniature humanoids are more prevalent, but employ less biomimicry in their design (e.g. fewer sensors, Degrees of Freedom, etc) and lack similar developments. This paper describes a compliant full-body telepresence control stack developed from the ground up for miniature humanoids. Framework experimentation on ROBOTIS OP3 hardware showcases walking at speeds up to 0.45 m/s independent of arm motions. Tele-loco-manipulation is demonstrated via a cube relocation experiment with an expert human operator. On average, the teleoperated system moved 2 different 40 g cubes within 10 mins, walking a total distance of 5 m. Overall, the developed system shows potential for miniature humanoid tele-loco-manipulation.
发表机构
- University of Louisville（路易斯维尔大学）
机构由 AI 辅助整理，请以论文原文为准。
