Real world robotics data for humanoid companies
High quality data engine powering robots that see, reason, and act with precision
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LLMs had the entire internet to initially gather knowledge and train on.
Robots don't. They need to learn how to think and move from the ground up.
Embodied AI requires large-scale human-demonstrated tasks, teleoperation trajectories, and annotations.
Our approach
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Human Demonstrated Data
Structured POV and third-person recordings of real people performing real-world tasks, captured with consistent hardware and QC for embodied learning.
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Robot Teleoperation
Multi-view teleop sessions from humanoids we operate; designed to generate clean manipulation trajectories for control and imitation learning.
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Annotated Action Segmentation
Fine-grained, multi-stream action labeling with natural-language descriptions in JSONL/CSV for VLA and policy training.
Use cases
High-quality human data fueling robotics in factories, households, warehouses, and more.
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Vision-Language-Action Model Training
Build models that understand actions, intent, and object relationships using fine-grained action segmentation with natural-language descriptions.
Imitation Learning & Policy Refinement
Leverage high-quality teleoperation trajectories to improve low-level control, dexterity, and object interaction.
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Environment-Specific Robotics Datasets
Acquire task libraries captured in kitchens, offices, and real lived-in spaces to match deployment environments.
Train Humanoid Manipulation Models
Use POV + multi-view human demonstrations to teach robots precise household and office manipulation tasks.
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