BifrostUMI：连接无机器人演示与人形机器人全身操作
BifrostUMI: Bridging Robot-Free Demonstrations and Humanoid Whole-Body Manipulation
AI总结：
研究针对人形机器人全身技能学习数据收集难题，提出无机器人框架BifrostUMI，利用轻量级VR设备和特制夹具收集数据，训练高级策略预测关键点，经实验验证其对可转移人形机器人全身技能学习的有效性。
摘要：
高质量演示数据对人形机器人技能学习至关重要，尤其对于需要协调感知、运动和操作的全身行为。现有数据收集方法大多依赖机器人遥操作，受硬件可及性、操作员专业知识和效率限制。受通用操作接口（UMI）启发，我们提出BifrostUMI，一个用于人形机器人全身数据收集的便携式无机器人框架。它使用轻量级VR设备和受UMI启发的夹具收集稀疏人体关键点轨迹、手腕视角观察和夹具动作。这些演示训练高级策略预测未来关键点，重新定位到机器人原生全身参考并由全身控制器执行。在五个真实场景中的实验证明了该框架的有效性，并验证了所收集演示用于可转移人形机器人全身技能学习的可行性。
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 BifrostUMI, a portable and robot-free framework for humanoid whole-body data collection. BifrostUMI 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。
