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6 added, 0 removed. Audit A to A.
---
name: data
description: Choose and structure training data for robot-learning projects.
---
# 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.
+ - When schemas and task labels cannot distinguish two environments, compare a
+ deterministic scene observation from the dataset with the pinned target
+ environment. Prefer a stable reference camera over a randomized wrist view.
- Generate in simulation when scale, controlled variation, or labels matter
more than perfect realism.
+ - If a documented search finds no dataset for the exact scene and control
+ contract, generate demonstrations in the pinned application environment and
+ retain only episodes that satisfy its success condition.
- 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](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.