Cost-efficient scaling
Deploy collection projects across factories, homes, retail and logistics without relying on large fleets of expensive robot bodies.
Body-free embodied AI data partner
XUNCAITEK
Real-world factory data collection for embodied intelligence. We combine first-person Ego video, wearable motion capture, multimodal synchronization and detailed annotation to deliver datasets ready for VLA training.
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, action, semantics and quality records.
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
Ego view, 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 setup
Standardize real task environments across factories, homes, retail, food service and logistics.
Wearable capture
Collect human operation with Ego glasses, motion capture, data gloves and VR/AR devices.
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 factory channels and project coordination capability, the company can rapidly connect and replicate collection scenes based on customer requirements.
Industrial manufacturing
Electronics assembly, wire harness insertion, sorting, packaging, welding, inspection and deformable object operations.
Craft and long-tail tasks
Bamboo and wood processing, stone carving, hand painting, folding and organizing tasks with high long-tail value.
Commercial and service spaces
Laundry plants, supermarkets, restaurants, warehousing, hotels and healthcare service environments for service robots.
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.
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.
On-site Wearable Views
External views complete the context around wearable collection.
A mixed field wall shows device wearing state, operator posture, camera coverage and the real work environment alongside the first-person data stream.
Delivery Standard
Built for training workflows, not raw-video handoff.
VLA
Vision Fields
Action Fields
Object Fields
Semantic Fields
Quality Fields
Metadata
99.5%
Annotation accuracy target
24h
Annotation turnaround
3-layer QA
Review workflow
Fields cover RGB video, frame IDs, camera parameters, hand trajectories, keypoints, action phases, contact states, object classes, natural-language instructions, anomaly clips and QA records.
Advantages
Scene resources, project management and data engineering together enable reliable 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 real-world factory 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.