Physical AI Training Data
End-to-end physical AI data: LiDAR annotation, 3D sensor fusion, in-cabin automotive intelligence, biometric human-centric data, and world model collection at scale.
Data Capabilities
Four specialised services for teams training embodied, physically-grounded AI systems.
3D Sensor Fusion & LiDAR Annotation
Precise 3D bounding boxes, point cloud segmentation, and multi-sensor fusion labeling for autonomous vehicles, drones, and robotic systems. Appen's annotators are trained to label across LiDAR, radar, and camera feeds with the consistency that safety-critical applications require.
Biometric Human-Centric Data
Ethically collected motion capture, facial expression labeling, gaze tracking, and gesture annotation that teach AI to understand human behaviour and physical intent. All collection operates under responsible AI consent frameworks.
In-Cabin Automotive Intelligence
Multi-sensor annotation for driver monitoring systems, occupant detection, gaze and gesture recognition, and voice command integration. Appen supports automotive OEMs and Tier 1 suppliers building the in-cabin AI layer for next-generation connected vehicles.
World Model Data Collection
Large-scale egocentric video, environmental scene capture, and interaction data for teams training world models and embodied AI agents. Appen's global field operations collect in diverse physical environments at the scale and diversity that world model training demands.
Ready-to-Use Datasets
Licensed off-the-shelf data available now or coming soon — accelerate development without starting from scratch.
Roomba View Images
Embodied robot-perspective images from a robotic vacuum cleaner, supporting navigation and environment grounding for physical AI.
Action Videos
Videos of humans and animals performing everyday actions, enabling action recognition and language-to-motion mapping.
Hand Gesture Videos
Large collection of hand gesture videos supporting non-verbal intent recognition and gesture-based control.
Case Studies
How leading AI organisations trust Appen for multimodal & physical ai data.
How Nearmap Scaled 3D Spatial Annotation for Geospatial AI
3D point cloud and aerial imagery annotation pipeline powering spatial understanding models - a core capability for autonomous navigation and world models.
Multimodal Data Pipelines for Cross-Sensor AI Alignment
Multimodal dataset creation and cross-modal alignment techniques applicable to LiDAR-camera fusion, robotics perception, and physical environment understanding. Read case study
How Onfido Built Bias-Mitigated Biometric Models at Scale
Biometric and human-centric data labeling for robust recognition systems - showcasing Appen's expertise in the human annotation pipelines critical to in-cabin automotive AI and robotics safety.
Ready to build with confidence?
Talk to our team about physical AI training data, from LiDAR annotation and sensor fusion to world model data collection at scale.