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Custom collections available.
The physical world,
instrumented.
DatraAI deploys egocentric wearable capture systems in factories, warehouses, kitchens, and service environments. We record synchronized multimodal streams — RGB video, stereo depth, 9-axis IMU, hand and body pose — then structure, timestamp-align, and annotate every frame to produce training-ready datasets for physical AI systems.
Collection
Egocentric wearable rigs
deployed on real workers.
Real tasks. Real motion.
Processing
Frame-level sync
across all modalities.
Annotation + QA pipeline.
Delivery
Ready-to-train format
HDF5 / JSON / MP4+metadata
Exclusive or non-exclusive.
Seven modalities.
One synchronized stream.
±2ms sync precision
across all streams
01
RGB VIDEO
Egocentric 1080p/30fps, 98° FOV wearable capture
1920×1080 · 30FPS · H.264
02
STEREO DEPTH
Binocular depth estimation for 3D spatial understanding
Disparity · Point Cloud · 30FPS
03
9-AXIS IMU
Accelerometer + gyroscope + magnetometer at 200Hz, timestamped
Accel · Gyro · Mag · 200Hz · ±2ms
04
HAND POSE
21-keypoint skeleton, per-frame grasp and manipulation labels
MediaPipe · 21 Keypoints · 30FPS
05
BODY POSE
Full-body skeleton for locomotion and posture datasets
33 Landmarks · 3D · 30FPS
06
ACTION LABELS
Task-level and frame-level action segmentation
Verb · Object · Phase · Confidence
07
TEMPORAL ANNOT.
Start/end timestamps, phase labels, interaction boundaries
Millisecond · Phase · Boundary
Collected across
the physical world.
Expanding to: Healthcare · Agricultural · Gig Workers · Residential
Data Pipeline
FROM WORKER TO WEIGHT UPDATE
01
CAPTURE
Wearable head-mounted rigs & sensors
02
INGEST
Lossless sensor stream aggregation & upload
03
SYNC
Hardware-level temporal synchronization (±2ms)
04
ANNOTATE
Semantic action parsing & joint kinematics
05
DELIVER
Unified HDF5 format & metadata schema
RGB · 30FPS
DEPTH · 30FPS
IMU · 200HZ
POSE · 30FPS
LABELS · FRAME-LEVEL
Research
Building models that
understand the world.
We are training a world model and a foundation model using the real-world demonstration data we collect. Our goal: build the credibility to partner with frontier robotics labs and accelerate the development of general-purpose physical AI.
WORLD MODEL
Training a physical world model on real human demonstration data — learning the geometry, dynamics, and cause-effect structure of manipulation environments.
IN PROGRESSFOUNDATION MODEL
A multimodal foundation model grounded in egocentric RGB, depth, IMU, and full-body kinematics — purpose-built for generalizable robot policy learning.
IN PROGRESSDATA FLYWHEEL
Continuously improving data quality and coverage by closing the loop between model feedback and collection protocols across new environments.
ONGOINGPartnering with frontier robotics labs · Research preprint coming soon
Product
Raw data in.
Training datasets out.
Our end-to-end processing pipeline takes raw, multi-modal sensor streams and transforms them into clean, structured, training-ready datasets — with every step automated, auditable, and optimised for robotics foundation model training.
01 / 05
QUALITY ASSURANCE
Automated frame-level and sequence-level checks. Flags blur, occlusion, sensor dropouts, and motion artifacts before any downstream processing.
1 of 5 stages
Output formats
HDF5 · RLDS · LeRobot · Custom Schema
Pipeline stages
QA → Sync → Annotate → Action Label → Package
Purpose
Raw data → Training-ready dataset
Dataset Catalog
Browse the
dataset catalog.
Access synchronized multimodal datasets from industrial, warehouse, household, and service environments. Exclusive and non-exclusive licensing available.
“The bottleneck for physical AI is not compute. It is data — real-world, embodied, multimodal, grounded in physical reality. DatraAI is building the collection and delivery infrastructure that makes training-ready physical AI data as accessible as compute.”
See what our data looks like
Free sample previews · No signup required