Data

Data

Begin with coverage: decide what behavior, embodiment, and conditions the policy must see before deciding how to collect them.

Choose the source

• Search existing datasets first. Confirm task, action space, degrees of freedom, gripper, cameras, state features, timing, license, and provenance.
• Use an exact embodiment match directly. Treat a near match as pretraining or co-training material, not a drop-in dataset.
• Generate in simulation when scale, controlled variation, or labels matter more than perfect realism.
• Collect on the real robot when contact, appearance, or hardware behavior is difficult to reproduce faithfully.
• Mix sources deliberately: simulation can provide coverage; a smaller real set can expose the remaining sim-to-real gap.

Protect the useful signal

• Constrain the task and workspace before adding more episodes. Dense coverage of the behavior matters more than a large headline episode count.
• For a successful-expert imitation dataset, keep only demonstrations that meet the task's success definition. Retry or discard oracle failures, and stop loudly if the success rate collapses. Do not apply this rule to DAgger, corrective, recovery, or failure-learning datasets that intentionally retain non-expert transitions.
• Define the episode boundary, observations, actions, rates, success label, splits, and target storage format before collection starts.
• Record the source revision and collection conditions. Dataset facts and licenses must come from the current card or repository, not memory.

Go deeper only when needed

• For the Robium evidence behind workspace density and demonstration quality, read COLLECTION-QUALITY.md.
• Use Hugging Face guidance only when the decision reaches Hub discovery, inspection, transfer, or publication.
• Use LeRobot guidance when the decision reaches LeRobotDataset recording, editing, training, evaluation, or platform-specific teleoperation controls.
• Use simulator guidance only after choosing simulation as a source; Isaac Sim and Gazebo own their generation mechanics.
• Test fixtures belong to test-assets, not this training-data decision.

Done

• The chosen sources cover the target embodiment and task, the gaps are named, and the first small collection or dataset slice can validate the plan before scale or paid compute.