Organizations: Institute for Artificial Intelligence, Peking University, Beijing 100871, China · School of Integrated Circuits, Peking University, Beijing 100871, China · School of Psychological and Cognitive Sciences, Peking University, Beijing 100871, China · Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100871, China · LeapZenith AI Research, Shanghai 201707, China · State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China · University of Chinese Academy of Sciences, Beijing 100049, China · School of Aerospace Engineering, Tsinghua University, Beijing 100084, China
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.
Figures & tables
Fig. 1 : System architecture and processing pipeline of the T-800 high-frequency tracking system. (a) The wearable platform features an anatomically aligned, flexible sensor array designed to minimize restriction on natural hand movements. (b–c) Mechanical isolation strategy. (b) Exploded and (c) cross-sectional views of the “sandwich” structure. The rigid metal shield and support plate mechanically decouple the sensor from the fabric and skin; tension forces (blue arrows) and bone reaction forces (purple arrows) are routed through the rigid housing, ensuring stable coupling. (d) Wrist-mounted sensor hub consolidating heavy components (battery, mcu ) to minimize inertial loading on the finger segments. (e) Topology of the 18-sensor array covering the full kinematic chain. (f) Spatiotemporal reconstruction workflow. The pipeline transforms raw, asynchronous, and drifting data streams into a coherent 800Hz signal via broadcast-based temporal calibration, followed by spatial alignment to reconstruct high-fidelity gestures.
Name
Sensor Type
IMU Type
Connectivity
Haptic Obstruction
Synchronization
Palm & Arm Sensors ↑
Finger Sensors ↑
IMU Count ↑
Sample Rate (Hz) ↑
iSEG-Glove [ 21 ]
IMU
MPU9150
Wi-Fi
Fully-Obstructed
No
1
3
16
40
M. Edmonds et al . [ 22 ]
IMU
BNO055
Wired
Fully-Obstructed
No
1
3 (2 for thumb)
15
20
G. Saggio et al . [ 23 ]
IMU
MPU9250
Wired
Cutout Rings
No
3
3
18
40
Z. Yang et al . [ 24 ]
IMU + Magnetic
MPU9250
Wired
Fingertip
Post-process
1+0
1+1
3
100
FI-HGR [ 25 ]
IMU + Flex
ICM20948
Bluetooth
Fully-Obstructed
Post-process
1+0
1+0 (1+1 for index)
6
50
TABLE I: Comparison of different hand motion capture systems with IMU-based sensing. An upward arrow indicates that higher values are better.
Fig. 2 : Broadcast-based temporal calibration mechanism for drift-free multi-sensor synchronization. (a–b) The desynchronization bottleneck. (a) Conventional architectures rely on sequential polling of individual nodes. (b) Each imu runs on an independent internal oscillator. Slight frequency variations accumulate into significant clock drift, causing nominally simultaneous samples to scatter across the timeline (temporal jitter). (c–d) Broadcast synchronization protocol. (c) The proposed architecture utilizes a simultaneous broadcast command to latch a global timestamp across all imu instantly. (d) These latched timestamps serve as shared anchor points, enabling the system to interpolate asynchronous raw readings onto a unified, strictly aligned temporal grid. (e–i) Validation via rapid dynamics. (e) A rapid hand-flipping task ( 140s duration) serves as a stress test. (f) Quantitative analysis of time offsets reveals diverging clock drift across the 18 sensors using standard one-time calibration. (g) Signal coherence analysis: the proposed method (blue) maintains tight synchronization of finger pitch angles, whereas the baseline (red) exhibits severe temporal divergence. This divergence manifests visually as (h) the drifted hand gesture in the baseline reconstruction, contrasted with (i) the sustained kinematic fidelity and sub-frame precision achieved by our broadcast approach.
Fig. 3 : Two-stage framework for calibrating sensor-to-bone mapping. (a) Zero pose calibration: the hand is placed flat on a horizontal surface in the zero pose. The frames W and IW differ only by a rotation θ about their common z -axis (aligned antiparallel to gravity). (b) θ calibration: the hand is rotated by angle α about the world-frame x -axis against a vertical YZ-plane ( e.g . , a wall) to determine the unknown parameter θ .
Fig. 4 : Validation of kinematic fidelity across the 33-grasp taxonomy. Systematic evaluation comparing physical hand configurations (left columns) with real-time reconstructions (right columns) captured by T-800. The dataset is stratified according to the Feix et al . taxonomy [ 34 ] into three biomechanical categories: Power grasps (red) requiring high-force closure involving the palm; Intermediate grasps (yellow) balancing active manipulation with stability; and Precision grasps (green) demanding fine-grained fingertip articulation. The consistent reconstruction accuracy across this diverse range confirms two critical system properties: (i) the mechanical design preserves the hand’s natural kinematic workspace without perceptible impedance; and (ii) the spatial calibration framework remains robust across the entire manifold of human hand poses.
Fig. 5 : Spectral discovery of high-frequency kinematics in human dexterity. (a) Anatomical sensor mapping of the 18-node array (MC: metacarpal, PP: proximal, MP: middle, DP: distal phalanx). (b–e) Biomechanical energy landscapes. Time-resolved spectral power summation for frequencies strictly >100\text{,}\mathrm{Hz} —a band theoretically invisible to conventional $200\text{\,}\mathrm{Hz}$ systems. (b–c) Pen spinning tasks (unidirectional and bidirectional) reveal distinct, high-amplitude spectral bursts spatially localized to the manipulating digits (Thumb, Index, Middle). These transient spikes coincide with rapid actuation phases, confirming that fine motor control relies on sub-millisecond micro-adjustments. (d) In a four-finger spinning task explicitly excluding the thumb, the thumb sensor exhibits spectral quiescence (flatline). (e) Catching a heavy object elicits a synchronized, global high-frequency response across the entire hand. Together, these profiles validate that signal energy $>$100\text{\,}\mathrm{Hz} is kinematic information essential for high-fidelity modeling. Detailed manipulation processes and reconstructed gestures are available in the Supplementary Video .
Fig. 6 : Kinematic retargeting validation across diverse robotic hand platforms. Representative frames from dynamic manipulation tasks demonstrating the mapping of captured human hand motions by T-800 (top row) to three robotic hand models with progressively diverse kinematic structures: Shadow Dexterous Hand, Allegro Hand, and Leap Hand. The sequential frames illustrate the algorithm’s ability to transfer high-frequency human manipulation dynamics to robotic platforms while respecting their kinematic constraints.
Tendon-driven underactuated hands excel in adaptive grasping but often suffer from kinematic unpredictability and highly non-linear force transmission. This ambiguity limits their ability to perform precise free-motion shaping and deliver reliable payloads for complex manipulation tasks. To address this, we introduce the PHANTOM Hand (Hybrid Precision-Augmented Compliance): a modular, 1:1 human-scale system featuring 6 actuators and 15 degrees of freedom (DoFs). We propose a unified framework that bridges the gap between precise analytic shaping and robust compliant grasping. By deriving a sparse mapping from physical geometry and integrating a mechanics-based compensation model, we effectively suppress kinematic drift caused by spring counter-tension and tendon elasticity. This approach achieves sub-degree kinematic reproducibility for free-motion planning while retaining the inherent mechanical compliance required for stable physical interaction. Experimental validation confirms the system's capabilities through (1) kinematic analysis verifying sub-degree global accuracy across the workspace; (2) static expressibility tests demonstrating complex hand gestures; (3) diverse grasping experiments covering power, precision, and tool-use categories; and (4) quantitative fingertip force characterization. The results demonstrate that the PHANTOM hand successfully combines analytic kinematic precision with continuous, predictable force output, significantly expanding the payload and dexterity of underactuated hands. To drive the development of the underactuated manipulation ecosystem, all hardware designs and control scripts are fully open-sourced for community engagement.
Teng Yan, Jiongxu Chen, Qixiang Hua +4
The Hong Kong University of Science and Technology (Guangzhou)
Capturing hand pose during dexterous manipulation remains difficult: vision-based methods degrade under occlusion and challenging lighting, while sensorized gloves, though occlusion-free, are prone to drift and magnetic interference and rarely match motion-capture accuracy. We introduce a fiber optic sensing glove for full hand pose tracking that targets these failure modes, using multi-core shape-sensing fibers that capture each fiber's full 3D shape rather than curvature alone. A novel pipeline registers each reconstructed fiber shape to a common hand reference frame, and a new inverse-kinematics solver reconstructs full hand pose at 60 Hz using curve constraints. Benchmarked on a 2-hour dataset of dexterous object manipulation tasks across 5 subjects, the glove achieves 7.2 mm mean fingertip position error against motion capture ground truth, reduced to 4.9 mm by a one-time factory calibration of the fiber routing hub that transfers across users and sessions. These capabilities enable high-fidelity data capture and bimanual virtual teleoperation - both essential to advancing the robotics field.
J. D. Peiffer, Taylor Niehues, Li Guan +2
Meta, Redmond, Washington, 98052, USA · Department of Biomedical Engineering, Northwestern University, Evanston, Illinois, 60208, USA · Work done at Meta
We present ART-Glove, an articulated tactile glove designed to capture contact-grounded dexterous demonstrations while preserving human dexterity. ART-Glove makes hand-side contact geometry explicit with 16 rigid functional surfaces covering the fingers, thumb, and palm. Twenty-two anatomically aligned joints connect these surfaces and allow them to follow human hand motion during dexterous manipulation. Encoder-based sensing tracks surface motion, while dense piezoresistive tactile sensing records contact over the same surfaces. The complete system captures synchronized 22-DoF joint measurements and 2048-taxel tactile measurements at 120 Hz. We evaluate ART-Glove across experiments on motion freedom, joint sensing, tactile sensing, and contact-rich interaction capture, demonstrating its ability to preserve human dexterity while recording contact-grounded information that can support downstream dexterous robot learning.