Glove Hand Capture
A higher-value setup for collecting precise hand, finger, and manipulation data using instrumented data gloves. Designed for robotics teams that need grasp sequences, finger pose, wrist orientation, and object interaction data for dexterous manipulation and imitation learning pipelines.
Equipment
What the setup includes
Data gloves (both hands)
finger joint and wrist angle sensors
Hand and finger motion capture
per-joint DOF at 30–100 Hz
Task and object tagging
via companion app or annotator
Optional validation camera
RGB cross-reference for QC
Structured export pipeline
URDF-compatible or custom schema
Best for
Ideal data types
- Finger pose estimation datasets
- Hand pose and wrist orientation
- Grasp sequence collection
- Repetitive manipulation episodes
- Object-specific interaction tasks
- Teleop-compatible hand data
- Dexterous task demonstrations
- Imitation learning priors
Industry applications
Where glove capture is deployed
Glove Hand Capture is used in environments where fine-grained hand and finger motion is the primary signal — assembly, manipulation, teleoperation, and dexterous task research.
Packaging & assembly
lid closing, part insertion, fastener tightening
Manipulation research
grasp taxonomy, in-hand re-orientation, transfer
Repetitive hand workflows
folding, wrapping, stacking, sorting by hand
Repair-like tasks
connector insertion, screw driving, cable routing
Teleoperation training
bimanual control, trajectory priors, recovery
Dexterous task pilots
tool use, precision grasp, compliant contact
Data structure
Episode metadata and frame schema
Each episode contains structured metadata and per-frame joint data. The schema can be extended with buyer-specific fields, object taxonomies, and annotation layers.
Frame arrays contain per-joint angle, position, and velocity. Sampling rate is configurable per program (30–100 Hz).
Integration
Robotics framework compatibility
Glove capture data is exported in formats compatible with common robotics training pipelines. Custom delivery structures are supported per program.
URDF export
Joint angle data exportable in URDF-compatible format
ROS-compatible
Episode streams compatible with ROS message formats
LeRobot / OpenPI
Dataset format alignment available on request
HDF5 / NumPy
Raw per-frame arrays in open scientific formats
Custom schema
Buyer-defined fields and delivery structure supported
Tradeoffs
Advantages and limitations
Advantages
- Precise per-joint angle and pose data
- High-value signal for manipulation models
- Stronger prior for imitation learning
- Compatible with URDF and robotics export formats
- Ideal for dexterous and teleop datasets
- Premium batch potential for robotics buyers
Limitations
- Higher equipment cost than video-only setups
- Longer contributor onboarding and calibration
- Setup more sensitive to environment constraints
- Not required for workflow or task video use cases
For task workflows not requiring fine-grained hand data, see Video Task Capture.
Quality control
Default acceptance criteria
Each glove capture episode passes a multi-point QC review before inclusion in the accepted batch. Criteria are extended per buyer spec.
- Glove correctly calibrated before session
- All required joints tracked throughout episode
- Task fully captured start to finish
- Object tag correctly applied
- No tracking loss or sensor dropout
- Per-frame data within expected DOF range
- Outcome label validated against episode
- Validation camera angle correct (if used)
- Sample rate consistent throughout session
- Metadata fields complete and schema-valid
Start a glove capture pilot
Tell us the target manipulation task, object types, and required joint resolution. We will scope a pilot and propose a collection protocol.