Exports

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What ships with each release

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Import a LeRobot dataset

• Each LeRobot episode becomes one .mcap file, which Avala ingests as one item. A LeRobot episode maps to an Avala sequence.
• Camera streams are written as foxglove.CompressedImage, so they render in the Mission Control viewer.
• Proprioception (observation.state, action and other 1-D numeric features) is written as google.protobuf.Struct messages. The values are preserved and readable as raw messages.
• Every other per-frame column (rewards, episode_uuid, failure_type and other text columns) is kept on its own topic. A control_source column (policy, teleop, intervention, hold) lands on /lerobot/control_source.

pip install "avala[lerobot]"

avala import lerobot \
  --repo-id your-hf-user/your-dataset \
  --name "Teleop rollouts" \
  --slug teleop-rollouts \
  --episodes 0,1,2 \
  --wait

from avala import Client
from avala.importers import import_lerobot

dataset = import_lerobot(
    Client(),
    repo_id="your-hf-user/your-dataset",
    name="Teleop rollouts",
    slug="teleop-rollouts",
    episodes=[0, 1, 2],
    wait=True,
)
print(dataset.uid, dataset.item_count)

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Export an Avala dataset to LeRobot

avala lerobot export your-org/your-dataset \
  --repo-id your-hf-user/your-dataset \
  --output ./lerobot-out \
  --fps 30 \
  --task "load the dishwasher" \
  --include-outcomes

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Use outcome labels in training

• Weight or select sequences by outcome, progress, quality and speed.
• Train on subtask label strings and hand-action action strings for language-conditioned policies.
• Split by leakage group when you build your own validation sets.
• Keep sequences with evaluation_membership set to held_out_eval out of training when you merge data from other sources. Releases never include them.