Perception and manipulation
Define task-specific data for object recognition, position and pose estimation, grasping, and material handling.
Training and Evaluation Data for Physical AI
Data for robots to learn and act
We create data for training and evaluating perception and motion in industrial robots and humanoids. From real-world images, sensor readings, and motion records to simulated synthetic data, annotations, and evaluation datasets, we design and prepare data around each platform and task.
APPLICATIONS
Where we can help
Humanoid motion learning
Prepare aligned observations, actions, and outcomes for tasks such as walking, balance, and object manipulation.
Environmental variation and exceptions
Build evaluation cases covering lighting, object layouts, occlusion, and failures relevant to operation.
WHAT WE DELIVER
What we deliver
Data requirements and collection planning
Define collection conditions, recording formats, and labels based on robot platforms, sensors, tasks, and evaluation metrics.
Real-world and motion data preparation
Capture and organize images, depth, sensor readings, joint states, and action logs according to the available hardware and collection environment.
Simulation and synthetic data
Generate training and evaluation data by varying objects, lighting, and sensors in simulation, and assess differences from real-world data.
Annotation and quality checks
Annotate objects, regions, poses, and action segments as needed, and check missing values, duplicates, and consistency.
Training and evaluation dataset assembly
Separate training and evaluation data and document conditions, versions, and preparation steps. Where needed, connect the data workflow to model tuning and evaluation.
DELIVERABLES
Typical deliverables
Task-specific datasets
Deliver images, sensor readings, motion records, and associated labels in agreed formats.
Evaluation scenarios and quality reports
Document collection and generation conditions, dataset composition, quality findings, and evaluation scenarios.
Repeatable data workflows
Where required, provide generation, conversion, and validation scripts or simulation environments to support ongoing dataset creation.
HOW WE WORK
Our approach
Designed around the platform and task
Define scope around training and evaluation needs, task conditions, and intended use—not volume alone.
Connect simulation with real-world validation
Distinguish real-world and synthetic data conditions and use evaluation findings to guide further collection, generation, and improvement.
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