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arXiv 2603.26403cs.RO
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T-800:一种用于精确手部姿态追踪的800Hz数据手套

T-800: An 800 Hz Data Glove for Precise Hand Gesture Tracking

Peking University(北京大学);Institute for Artificial Intelligence, Peking University(北京大学人工智能研究院);School of Integrated Circuits, Peking University(北京大学集成电路学院);School of Psychological and Cognitive Sciences, Peking University(北京大学心理与认知科学学院);Beijing Key Laboratory of Behavior and Mental Health, Peking University(北京大学行为与心理健康北京市重点实验室);LeapZenith AI Research(跃升人工智能研究院);State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences(中国科学院沈阳自动化研究所机器人学国家重点实验室)

Haoyang Luo, Zihang Zhao, Leiyao Cui, Saiyao Zhang, Liu Yang, Zhi Han, Xiyuan Tang, Yixin Zhu

AI总结:

T-800通过800Hz高带宽数据手套实现同步全手运动追踪,解决高频率动态捕捉难题,恢复细粒度操作细节,为机器人控制提供丰富行为数据。

摘要:

人体灵巧性依赖于快速、亚秒级的运动调整,但捕捉这些高频动态仍是生物力学和机器人学中的持续挑战。现有运动捕捉方法因时间分辨率与视觉遮挡之间的权衡而无法记录快速、接触密集的操作的细粒度手部运动。本文介绍T-800,一种高带宽数据手套系统,可在800Hz下实现同步全手运动追踪。通过整合新型广播同步机制与机械应力隔离架构,系统在长时间剧烈运动中保持18个分布式惯性测量单元(IMUs)的亚帧时间对齐。我们证明T-800恢复了因时间采样不足而丢失的细粒度操作细节。分析表明,人体灵巧性表现出显著的高频运动能量(>100Hz),这由于先前硬件限制下的奈奎斯特采样极限而无法触及。为验证系统在机器人操作中的实用性,我们实现了将T-800的高保真人类手势映射到灵巧机械手模型的运动学重定向算法。这证明高频运动数据可以准确转换并尊重机械手的运动学约束,为未来训练鲁棒控制策略提供必要的丰富行为数据。

英文摘要:

Human dexterity relies on rapid, sub-second motor adjustments, yet capturing these high-frequency dynamics remains an enduring challenge in biomechanics and robotics. Existing motion capture paradigms are compromised by a trade-off between temporal resolution and visual occlusion, failing to record the fine-grained hand motion of fast, contact-rich manipulation. Here we introduce T-800, a high-bandwidth data glove system that achieves synchronized, full-hand motion tracking at 800 Hz. By integrating a novel broadcast-based synchronization mechanism with a mechanical stress isolation architecture, our system maintains sub-frame temporal alignment across 18 distributed inertial measurement units (IMUs) during extended, vigorous movements. We demonstrate that T-800 recovers fine-grained manipulation details previously lost to temporal undersampling. Our analysis reveals that human dexterity exhibits significantly high-frequency motion energy (>100 Hz) that was fundamentally inaccessible due to the Nyquist sampling limit imposed by previous hardware constraints. To validate the system's utility for robotic manipulation, we implement a kinematic retargeting algorithm that maps T-800's high-fidelity human gestures onto dexterous robotic hand models. This demonstrates that the high-frequency motion data can be accurately translated while respecting the kinematic constraints of robotic hands, providing the rich behavioral data necessary for training robust control policies in the future.

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。

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