Accelerate your model improvement with physical AI data that is precisely curated, calibrated and measured to maximize the impact on your policy.
Precise - Diverse - Measured
A DROID manipulation study showed that task-matched data selection improved the task result from 0 to 85%.
A MimicLabs manipulation study showed that retrieved skill-subset selection achieved the same or better downstream performance using about one-tenth of the data.
An EgoMimic manipulation study showed that adding egocentric hand data outperformed adding more robot-only data, with up to 200% relative improvement, versus adding more robot-only data, in the studied tasks and baselines.
Define the task, embodiment, and success bar your policy needs.
We select across vendors for the episodes that fit the spec.
Annotation, structuring, and format conversion to your training pipeline.
A test record ships with every dataset so you know it will train.
Real robot trajectories with synchronized video, state, action, and task context.
Egocentric, UMI-style, VR, glove, and motion-capture demonstrations of real-world interaction, delivered with consent, provenance, usage-rights, and de-identification checks.
Failures, interventions, retries, near-misses, and recovery episodes.
Dual-arm, hand, finger, tool-use, and fine manipulation data for high-skill physical tasks.
Force, tactile, pressure, slip, and deformable-object interaction data.
Matched behaviors across robot arms, hands, mobile manipulators, humanoids, and other platforms.
Miraxis sits between your model goal and a fragmented robotics data supply market. Every dataset gets a release decision: blockers stop release, approved exceptions stay visible, and nothing passes silently.