用虚拟现实与强化学习实现小型人形机器人远程操控与行走
Towards Miniature Humanoid Tele-Loco-Manipulation Using Virtual Reality and Reinforcement Learning
- 自研全身体感控制框架，结合VR上肢遥控与强化学习下肢平衡
- 在OP3机器人上实现0.45米/秒行走速度，手臂动作不影响行走
- 专家操作可10分钟内搬运两个40克方块，总行进5米
近年来，全尺寸人形机器人能力迅速提升，致力于在人类环境中实现通用部署。主流控制方法由制造商采用：使用虚拟现实进行上肢遥操作，利用强化学习实现下肢平衡与行走控制，使单个远程操作者能感知、操作并导航真实远程环境。然而，该控制架构通常仅用于昂贵的全尺寸机器人，多数研究者难以获取。小型人形机器人虽更常见，但设计中生物相似性较低（如传感器少、自由度少等），且缺乏类似技术发展。本文从零开始构建适用于小型人形机器人的柔顺全身体感控制框架。在ROBOTIS OP3硬件上的实验表明，系统可在不依赖手臂运动的情况下实现最高0.45米/秒的行走速度。通过立方体重定位实验验证了远程协同操控能力：平均而言，专家操作者在10分钟内成功移动两个40克立方体，总行进距离达5米。整体表明，所开发系统在小型人形机器人远程协同操作方面具有潜力。
原文摘要 · Abstract (English)
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.
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