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name: data-preparation description: "Prepare, validate, convert, and batch Motus robot, latent-action, RoboTwin, and LeRobot data, including frame sampling, normalization, language features, and three-camera inputs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Motus data preparation

Use this route when a task involves Motus dataset layout, episode validation, RoboTwin conversion, LeRobot preparation, image-view concatenation, action normalization, or the batch contract consumed by training and inference.

• Read data-formats.md before selecting a dataset type or diagnosing missing files.
• Read api-reference.md for the dataset factory, collate output, sampling formulas, and image helpers.
• Read workflows.md for conversion, LeRobot cache, and three-view recipes. Networked or mutating source workflows are described but not silently run.
• Read troubleshooting.md when an episode is rejected, language/image alignment fails, or an optional dependency is absent.
• For model inputs and inference output, continue to model-inference; for YAML training launch configuration, use training.

Operating procedure

1. Identify the dataset family (robotwin, ac_one, aloha_agilex_2, latent_action, or lerobot) and whether the task is training, validation, conversion, or inference preparation.
2. Validate the directory structure and one-to-one episode resources before starting a loader. Preserve the same episode id across video, qpos/action, language embedding, and instruction text where that family requires it.
3. Set common.num_video_frames, temporal downsampling, and video_action_freq_ratio together. The action chunk is derived, not an independent guess; verify its shape against common.action_dim.
4. Keep language resources aligned: a selected embedding variant must correspond to the matching instruction text when VLM inputs are built. Pre-encoded WAN T5 embeddings are distinct from Qwen VLM processor inputs.
5. Run the bundled camera helper for three-view images when the downstream model expects a concatenated frame. Check the output dimensions before writing a dataset.
6. Treat download, conversion, video encoding, and T5-cache commands as explicit side-effect operations. Confirm paths, free space, and backups before running them; use the reference recipes rather than assuming they are harmless smoke tests.

The public runtime skill is self-contained: original repository scripts and checkout paths are evidence only, not commands future agents should reopen.