Physical AI training data
Physical AI training data is sensor-synchronized, spatially calibrated data (egocentric video, wrist pose, gripper state, LiDAR, camera, and radar) used to train robots and embodied systems to perceive and act in the real world.
Physical AI data capabilities
The embodiment data layer for teams building robots, autonomous systems, and world models. Appen produces the spatially precise, sensor-rich, human-demonstration data that lets your models act in the physical world, captured, synchronized, and annotated end-to-end in purpose-built facilities, backed by 30 years of data provenance.
Physical AI training data is sensor-synchronized, spatially calibrated data (egocentric video, wrist pose, gripper state, LiDAR, camera, and radar) used to train robots and embodied systems to perceive and act in the real world.
Egocentric data collection
Stereo first-person capture with synchronized wrist pose and gripper state. This is the manipulation data humanoid and embodied systems learn from.
Robotics training data
Human task demonstrations across kitchen, assembly, and logistics scenarios, annotated with object states and hand trajectories.
Sensor fusion data
Temporally aligned camera, LiDAR, and radar sequences for autonomous perception.
LiDAR annotation
3D bounding-box and semantic labeling on point clouds for autonomous and geospatial AI.
Autonomous vehicle data
Annotated multi-sensor driving sequences across urban, suburban, and highway environments.
World model data
Interaction and trajectory data for training and evaluating world models.
How we build physical AI datasets
One pipeline, under one roof: participant recruitment, hardware-synchronized capture, multi-stream recording, and expert frame-level annotation.
- Hardware-synchronized capture: stereo egocentric rigs, wrist-pose and gripper-state sensors, and multi-camera arrays recorded to a shared clock, delivered with sub-millisecond temporal alignment metadata.
- Sensor calibration: every dataset ships with intrinsic/extrinsic calibration and calibration standard/method - name it so streams are spatially registered out of the box.
- Contributor network: task demonstrations sourced from Appen's global contributor network of 1M+ people across 170+ countries, enabling diverse, natural interaction patterns.
- Governance & consent: participant consent, on-site data handling, and privacy controls aligned to GDPR / relevant framework; see data security.
On-site facilities
Controlled kitchen, workshop, assembly, and logistics environments let your team capture the manipulation and interaction tasks that matter for general-purpose robotics, at production scale, without standing up capture infrastructure yourself.
Ready-to-use physical AI datasets
Licensed off-the-shelf data available now or coming soon — accelerate development without starting from scratch.
European license plate detection annotations
100,000 license plate bounding boxes across 38,000 frames from the KITTI and Cityscapes autonomous-driving datasets, annotated in-house with box size and position metadata.
Roomba view images
Floor-level obstacles — particles, spills, pet food and pet waste — captured from a robotic vacuum's own camera perspective at 2K+ clarity.
CAD files
Multi-part 2D and 3D CAD projects spanning robotics, mechanical, electrical, architectural and aerospace design, drawn from real enterprise usage.
Insights & Resources
Data-Centric Computer Vision
A data-centric approach to model development: systematically changing and enhancing datasets to improve output accuracy, rather than only tuning the model.
Exploring the World of LiDAR
How LiDAR sensing works, from pulse to point cloud to 3D model, and where it is applied in autonomous driving, agriculture and construction.
Combating Wildfires with Computer Vision
A global security and aerospace company used multi-sensor integration, with persistent object identity across datasets, to predict where a fire will spread and how fast.
AI Solutions for Automotive
Customer-centric AI for autonomous vehicles and smart cars, and why consumer-experience applications are the most common route to deploying at scale.
Physical AI data FAQ
What approach does Appen take to building physical AI training data?
What data do we provide to train a robot or embodied AI system?
How do we collect egocentric data?
What sensors do you support for sensor fusion?
Can I license off-the-shelf physical AI datasets?
How do you ensure annotation quality?
Ready to build with confidence?
Talk to our team about physical AI training data, from egocentric capture and sensor fusion to robotics trajectories and off-the-shelf datasets.