Cost-efficient scaling
Deploy collection projects across industries, homes, retail and logistics without relying on large fleets of expensive robot bodies.
Body-free embodied AI data collection agency
XUNCAITEK
All-industry real-world data collection with EGO, UMI, multimodal synchronization and fine annotation to support scalable body-free data collection.
Real Scenes
Wearable Capture
Multimodal Sync
Fine Annotation
Standard Delivery
Industry Bottleneck
Robots need physical-world data before they can truly work.
Model capability is moving fast, but action trajectories, visual observations, hand-object relations, task semantics and synchronized real-world data remain scarce. Body-free collection turns human operation into scalable robot priors.
Real long-tail scenes
Capture human operation and environmental interaction in the real world to reduce the Sim2Real gap left by synthetic data.
Trainable assets
The deliverable is not just video. It is a data asset with vision, speech, action and semantics.
Company Solution
A data collection and annotation engineering platform for VLA training.
XUNCAITEK serves robotics companies, embodied foundation model teams and research institutions with real scenes, wearable capture, multi-device time sync, detailed annotation, QA review and dataset packaging.
Scene sourcing
Coverage across manufacturing hubs including Fujian, Guangdong and Zhejiang.
Multimodal capture
Supports first-person views, multi-camera video, motion capture, data gloves and VR/AR equipment.
Format adaptation
Support for RLDS, LeRobot, HDF5, Parquet and custom schemas.
How It Works
Five steps from scene to trainable dataset.
Scene linking
Standardize real task environments across industries, homes, retail, food service and logistics.
Wearable capture
First-person views, multi-camera video, motion capture, data gloves and VR/AR equipment.
Time sync
Align multi-camera and multi-sensor streams with unified timestamps for sequential training data.
Annotation
Segment actions, label objects, encode task semantics and record success, failure and anomalies.
Dataset delivery
Package, validate and update datasets in mainstream VLA formats or customer-defined schemas.
Scene Network
From standard production lines to complex craft workshops.
With stable cross-industry scene channels and project coordination capability, the company can rapidly connect and replicate collection scenes based on customer requirements.
EGO Sample Library
A field view of real collection scenes.
First-person capture samples from production, service, agricultural and household tasks show the physical detail needed for embodied AI training data.
Examples for display only. Selected samples are desensitized and do not disclose complete customer deliverables.
Representative sample
Hands, tools and workstations captured from the operator's view.
The gallery preserves occlusion, lighting, hand-object contact and workflow variation from real work sites.
Completed Projects
Wearable collection scenes from projects already in motion.
Photos are used only to show collection staff wearing devices in completed project environments.
Delivery Standard
Built for training workflows, not raw-video handoff.
Training sample · frame-synced
Synchronized VLA review
Frame synced
Video + frame labels
Hand keypoints · object boxes · contact candidates
3D bimanual glove pose
Source glove-surface model · synchronized to the video frame
Video-aligned
Left hand
Right hand
Drag to orbit · Shift/right-drag to pan · wheel or pinch to zoom
Task semantics
88% confidence
Coil the white cable.
Embodiment-neutral action
Weak supervision
Per-hand Δ pose + grip; not robot joint ground truth.
Advantages
Integrated scene resources, project management and data engineering ensure stable data delivery.
- Diverse scenes: standard production lines, flexible manufacturing, service spaces and complex craft workflows.
- Wide regional reach: rooted in Fujian and extended to manufacturing clusters in Guangdong and Zhejiang.
- Complete experience: end-to-end service capability for all-industry real-world Ego data collection.
Co-build
Turn real scenes into data infrastructure robots can learn from.
XUNCAITEK looks forward to working with robotics companies, embodied model teams, research institutions, scene partners and local industry ecosystems to build benchmark datasets for industrial, home and commercial robots.