Task-specific collection
Protocols built around a defined learning or evaluation gap.
Real-world data for Physical AI
Egocentric Data from Real-World Operations
AVIROB designs and operates custom egocentric data-collection programs across European manufacturing, warehousing, assembly and logistics environments.
//Egocentric Capture//LeRobot / RLDS Ready//Action Segmentation//Natural Language Labels//Hand Keypoints//VLA Models//Custom Collection Programs//Permissioned EU Collection//Egocentric Capture//LeRobot / RLDS Ready//Action Segmentation//Natural Language Labels//Hand Keypoints//VLA Models//Custom Collection Programs//Permissioned EU Collection
750+
Potential participants through operating partners
4
Core operational domains
Custom
Protocols designed around each model requirement
EU
Permissioned real-world collection network
Program design
Built around the data your model is missing
We work with robotics and AI teams to define the task, environment, sensing setup, annotation ontology and delivery schema before collection begins. Programs can include routine execution, task variation, failure, correction and recovery.
Real operational environments
Manufacturing, warehousing, assembly and logistics workflows.
Multimodal capture
RGB, depth, IMU and audio depending on the program and hardware.
Training-ready delivery
Temporal segments, annotations, metadata, provenance and QC documentation.
What we collect
Real work. Real environments.
Human experience, captured for Physical AI. We capture high-quality egocentric demonstrations of people performing real-world tasks across industrial and operational environments — turning human interaction with the physical world into structured data for robot learning.
Manufacturing
Real production workflows and human-machine interaction.
Warehousing
Picking, sorting, packing and material handling.
Assembly
Dexterous manipulation, insertion, fastening and multi-step assembly.
Tool Use
Natural human interaction with tools, components and equipment.
Inspection
Measurement, verification and quality-control workflows.
Logistics
Material movement, handling and fulfillment operations.
See the data
Egocentric data, captured and annotated in the real world
A close look at the visual quality, viewpoint and annotation detail we deliver from real manufacturing workflows.
Original footage and structured annotations shown side by side—from hand-object interaction to tools, machinery and task context.
How it works
From requirement to delivered dataset
A clear operating sequence connects program definition, real-world capture, structured processing and validated delivery.
DEFINE→CAPTURE→PROCESS→ANNOTATE→VALIDATE→DELIVER
Why egocentric
The camera goes where the hands go
Third-person footage loses the exact thing embodied models need: the actor's viewpoint, gaze, reach and contact moments. First-person capture keeps the causal link between intention and action intact.
- —Viewpoint matches the robot's own sensor placement
- —Hand-object interaction visible at contact resolution
- —Natural failure and recovery behaviour, not scripted takes
- —Long-horizon tasks recorded end to end
Pilot output
What a pilot delivery can include
—Raw and segmented egocentric video
—Synchronized sensor streams
—Task and action segmentation
—Hand-object interaction annotations
—Natural-language task descriptions
—Failure and recovery labels
—Session-level metadata and provenance
—LeRobot, RLDS, WebDataset or custom schema
—Dataset card and QC report
Need data no one has collected yet?
Tell us the task, the environment and the model you're training. We'll design the capture protocol and return a pilot batch.
Request a pilot