Demonstrations arrive raw and unsynchronized
Episodes land unsynchronized and unsegmented, and your researchers spend weeks preprocessing before a single gradient step runs.
Training Data for
Embodied AI
From teleoperation episodes to VLA fine-tuning – expert-reviewed robot data with the QA depth production hardware demands.
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Operational priorities
A data operation built for embodied AI
The conditions that separate a durable data operation from a one-off labeling project.
Unreviewed demonstrations cause policy regressions
Policy regressions trace back to sloppy teleop episodes that nobody reviewed before they entered the training set.
Field failures never make it back to the dataset
Lab benchmarks look fine while the warehouse deployment keeps stalling on the same grasp errors, month after month.
Capabilities
What we deliver for robotics teams
Collection
Teleoperation & demonstration data
Pilot, segment, and score manipulation episodes against your skill taxonomy.
Quality control
Episode QA at scale
Grade success, recovery, and failure before trajectories enter training.
Fine-tuning
VLA fine-tuning datasets
Build timestamped actions, instructions, and observation–action pairs in your schema.
Feedback
Deployment feedback loop
Turn field failures into targeted collection for the next training cycle.
The Loop
The demonstration loop
Four stages, and each one produces something your training pipeline can consume directly.
Spec & pilot
Task, embodiment, sensors, and skill taxonomy defined with your team. A pilot batch validates the collection protocol before anything scales.
Collect & segment
Episodes are ingested, synchronized, and segmented into subtasks – classified and schema-aligned by default.
Grade & label
Every trajectory is QA-graded; action captions and grounded instructions are added to spec, with IAA and gold-set metrics reported per batch.
Evaluate & re-target
Embodied eval sets score the new checkpoint. The gaps they surface set the target list for the next collection cycle.
One accountable partner
Built for the robot-learning lead
One team owns the pipeline from teleop rig to eval report, and your team stays on the model.
Scope a pilot batch
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Platform, experts, and workflows – unified in one secure infrastructure.