Abstract
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 subtantial motion energy components above 100 Hz that was 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. These experiments 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.
Zihang Zhao
Ph.D. '22
My research interests include robotics, mechatronics, and tactility-related robot cognition, etc.
Leiyao Cui
Ph.D. '24, co-advised with Prof. Zhi Han
My research interests include scene understanding, robotics, etc.
Saiyao Zhang
Ph.D. '23
My research focuses on robot manipulation, reative motion planning, aiming to develop advanced algorithms for enhancing robotic dexterity and efficiency in dynamic, real-world environments.
Zhi Han
Professor
Xiyuan Tang
Assistant Professor
