HumanoidUMI：连接无机器人示范与人形全身操作
HumanoidUMI: Bridging Robot-Free Demonstrations and Humanoid Whole-Body Manipulation
AI总结：
提出HumanoidUMI，一种便携无机器人的框架，利用VR设备和UMI夹爪收集人类关键点轨迹、腕部观察和夹爪动作，训练高层策略预测未来关键点并映射到机器人全身参考，实现人形全身技能学习。
摘要：
高质量的示范数据对于人形机器人技能学习至关重要，尤其是需要协调感知、移动和操作的全身行为。现有的数据收集方法主要依赖机器人遥操作，受限于硬件可访问性、操作员专业知识和有限的效率。受通用操作接口（UMI）启发，我们提出了HumanoidUMI，一种便携且无机器人的框架，用于人形全身数据收集。HumanoidUMI使用轻量级VR设备和受UMI启发的夹爪来收集稀疏的人体关键点轨迹、腕部视角观察和夹爪动作。这些示范训练一个高层策略来预测未来关键点，这些关键点被重新映射到机器人原生全身参考，并由全身控制器执行。在五个真实世界场景中的实验证明了所提出框架的有效性，并验证了收集的示范可用于可迁移的人形全身技能学习。
英文摘要：
High-quality demonstration data are essential for humanoid robot skill learning, especially for whole-body behaviors that require coordinated perception, locomotion, and manipulation. Existing data-collection methods largely rely on robot teleoperation, which is constrained by hardware accessibility, operator expertise, and limited efficiency. Inspired by the Universal Manipulation Interface (UMI), we propose HumanoidUMI, a portable and robot-free framework for humanoid whole-body data collection. HumanoidUMI uses lightweight VR devices and UMI-inspired grippers to collect sparse human keypoint trajectories, wrist-view observations, and gripper actions. These demonstrations train a high-level policy to predict future keypoints, which are retargeted to robot-native whole-body references and executed by a whole-body controller. Experiments in five real-world scenarios demonstrate the effectiveness of the proposed framework and validate the collected demonstrations for transferable humanoid whole-body skill learning.
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。
