Tracking and Data
- Tracking principle
- Electromagnetic-field (EMF) sensing
- Sensor configuration
- Five VetraSense sensors
- Hand pose reconstruction
- 25 DOF
- Fingertip pose
- 3-axis position and 3-axis rotation per fingertip
- Minimum resolution
- Below 0.2 mm
VetraSense / EMF Spatial Tracking
VetraGlove Pro Teleoperation Glove
VetraGlove Pro is a Qnbot teleoperation data glove built on electromagnetic-field (EMF) sensing. It captures 25-DOF hand pose and fingertip 6-DOF pose for robotic hand control, demonstration capture, and robot training data workflows.
Request Product ConsultationCore Specifications
System and Interfaces
- Sampling rate
- 180 Hz without vibration feedback
- Wired latency
- 10 ms
- Wireless latency
- 30 ms
- Wired connection
- USB 2.0 Type-C
- Developer support
- SDK licenses for Windows, macOS, and Linux
Wearable Hardware
- Glove type
- Full-finger glove
- Fingertip mounting
- Detachable fabric fingertip modules
- Size options
- M / L
- Weight
- Medium glove, excluding battery, below 100 g
Wireless Battery Module
- Battery capacity
- 1200 mAh
- Battery life
- 2.5 hours
- Charging time
- 2 hours
- Connection
- SparkLink technology
Operating Conditions
- Operating temperature
- 0°C–50°C
- Operating humidity
- 10%–80%, non-condensing
- Fabric cleaning
- Washable after removing electronic modules
Product Capabilities
25-DOF Hand Pose from EMF SensingFive VetraSense sensors use EMF spatial tracking to reconstruct hand pose and fingertip 6-DOF data.
Low-Latency, High-Rate Output180 Hz output, 10 ms wired latency, and 30 ms wireless latency support real-time control evaluation.
Fingertip Vibration FeedbackAdjustable fingertip vibration can support operator feedback during teleoperation experiments.
Modular, Serviceable HardwareDetachable sensors, glove modules, and battery components simplify maintenance and cleaning.
Robot Teleoperation, Training Data, and Dexterous Hand Integration
Robot Teleoperation
Robot TeleoperationMap fingertip pose and hand motion to a robotic dexterous hand for grasping, tool use, and fine-manipulation validation.
Training Data
Robot Training Data CaptureRecord timestamped hand-operation trajectories for demonstrations, replay, and robot-learning workflows.
Hand Integration
Dexterous Hand IntegrationReview DOF mapping, control protocols, coordinate conventions, calibration, and SDK connection before deployment.
Next step: Share your robot platform, purchase region, and use case. dexvivid will review product fit before documentation, demo, and quote discussions.
Discuss Purchase and Integration