Real-world training data collection for Physical AI
In-field operators, teleoperation facilities, and configurable lab environments. Encord collects the embodied, egocentric, and sensor data your robotics and physical AI models actually need.
Data is a commodity. Training-ready data is not.
Encord works inside the training pipeline – we know the difference between commodity data and the data that will actually move your model. Collection infrastructure is built from the training pipeline backwards, ensuring that every episode is classified, synchronised and delivery-ready.
Physical AI data types we collect
Embodiment-specific data
Operators run daily tasks with your robot, minimising the need for cross-embodiment skill transfer, and produce the highest-fidelity training signal for your deployment configuration.
Teleoperation data
Leader/follower teleoperation machines operated by trained humans performing high-dexterity tasks. Ideal for manipulation, grasping, and fine motor skill training.
Egocentric data
First-person video and sensor data of humans performing tasks in household, industrial, and commercial environments. Collected using head and wrist cameras at 1080p/30FPS.
UMI data
Handheld gripper systems that capture robot-analogous manipulation data without requiring a full robot setup. We support standard UMI, multi-finger variants, and client-provided gripper hardware at scale.
End-to-end collection coverage
Bespoke protocol design
We design the collection protocol with your team at our facilities before scaling – iterating on task definitions, hardware configuration, and quality criteria in a controlled environment before operators go into the field.
In-field operator network
Encord has thousands of trained operators available across kitchens, warehouses, offices, vehicles, and industrial settings – fully scalable to your volume requirements.
Encord’s lab facilities
We run dedicated facilities with configurable pods for kitchen, laundry and industrial environments – all equipped with flexible LED lighting and a variety of teleoperation machines, including stationary and mobile leader/follower arms.
Standardized equipment
Every deployment ships with a tested hardware kit configured to your protocol – cameras, grippers, mounts, and synchronization. Base kit includes RGB-D stereo depth cameras with IMU and multi-camera sync, paired with UMI grippers. Higher frame rates and multi-finger capture hardware available for models that need it.
Zero ingestion overhead
Collected data flows directly into Encord's platform – ready to filter, curate or route to human or model-assisted annotation. The ingestion work that costs most Physical AI teams weeks before labelling can begin doesn’t exist here.
Close the deployment loop
Every model fails in the field eventually. When yours does, we capture those failure modes through remote teleoperation and feed them back into the data pipeline. And by updating collection and annotation policies to address them, we make your model reliable in deployment, not just in the lab.
One platform.
Full data pipeline.
Enterprise-grade.
Built for scale.
Designed for reliable AI.
Built for scale.
Designed for reliable AI.
API/SDK-first. Zero data migration. Your data stays in your cloud.
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Frequently asked questions
Four core types: embodiment-specific data, teleoperation data, egocentric data, and UMI handheld gripper data, including multi-finger variants and customer-provided hardware. Each type maps to a stage of model training, from broad pre-training to embodiment-specific fine-tuning.
Collection always starts at Encord facilities, not in the field. Tasks, hardware setup, and quality criteria are defined with your team, then piloted in a controlled lab environment before scaling. Iteration happens before operators deploy, so cost and time aren't spent recollecting data that doesn't match your training objective.
The collection is designed backwards from the training pipeline, with every episode classified, synchronised, and ready to use by default. Data flows directly into the Encord platform, ready to filter, curate, and route to annotation, cutting out the weeks of pre-processing that typically delay training.
Diversity is built into protocol design, environments, lighting, operators, embodiments, and task variations are specified upfront, not left to chance during collection. Encord operates across multiple geographies and embodiment configurations, with hardware vendor partnerships (including teleoperation arm and biometric sensor providers) to close specific gaps in your training set.
Yes! When a model fails in deployment, failure modes are captured through remote teleoperation and human review, then fed back into the data pipeline. Collection and annotation policies update automatically based on what's failing, so retraining data reflects where your model is breaking in the real world.
Design your
collection protocol
We start with your task definition, deployment environment, and hardware configuration. From there we design the collection protocol, pilot it at our facilities, and scale.