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name: "rlds-export" description: "This skill exports LeRobot datasets to RLDS/TFDS with feature mapping, image and depth normalization, episode boundaries, metadata, and optional Apache Beam controls." disable-model-invocation: true metadata: disco-role: operating license: MIT

LeRobot → RLDS/TFDS export

Use this route when a user needs a local LeRobot dataset represented as an RLDS/TFDS dataset for an RLDS consumer such as an offline-RL, imitation-learning, or VLA pipeline. It covers the exporter contract and safe planning; it does not ship or execute a full data-conversion wrapper.

Boundary and routing

• Input: a readable local LeRobot dataset root with metadata, episodes, and the feature columns required by the requested RLDS schema.
• Output: a TFDS/RLDS dataset directory, normally with a train split and TFRecord storage, plus the requested dataset metadata.
• Not covered: RLDS/TFDS or Open X-Embodiment data converted to LeRobot. For that reverse direction, route to openx-conversion.
• Do not infer missing images, depth, state, action, episode lengths, or task text. Stop for a schema decision when a required field is absent or ambiguous.
• Treat an output directory as disposable until the plan confirms it is empty or separately approved for replacement. Never push to a Hub from this route.

Read the linked references before choosing flags:

• Workflows and runner controls
• RLDS schema and feature mapping
• Troubleshooting and recovery

Required preflight

1. Confirm the source and destination are different, writable local paths. Keep the source unchanged and make a backup or new destination for retries.
2. Inspect LeRobot metadata without iterating frames: feature keys, shapes, dtypes, image/depth layouts, action cardinality, episode keys and lengths, and whether every frame has a task string.
3. Confirm the exporter-compatible LeRobot API. The evidence implementation imports LeRobotDataset and LeRobotDatasetMetadata from lerobot.datasets.lerobot_dataset; other LeRobot versions may expose these symbols elsewhere. Resolve this import before any conversion attempt.
4. Install TensorFlow and TensorFlow Datasets for TFRecord/RLDS generation. Keep Apache Beam optional unless the user explicitly accepts its trade-offs.
5. Decide direct versus Beam before writing. Direct mode is the default and the required choice for small datasets or lossless episode preservation.
6. Choose a valid TFDS builder name (--task-name) and semantic version (--version, x.y.z). Record the encoding and all descriptive metadata.

If any preflight item is unknown, produce a validation plan rather than starting an export. Do not use a successful import as evidence that a real dataset maps correctly.

CLI contract

Use these arguments with the project’s equivalent exporter entry point; this sub-skill intentionally does not provide a wrapper that performs large writes:

• --src-dir PATH: local LeRobot dataset root; required.
• --output-dir PATH: TFDS output directory; required and should be new.
• --task-name NAME: RLDS/TFDS dataset or builder name; required.
• --version X.Y.Z: dataset version; default 0.1.0 in the evidence workflow.
• --encoding-format {jpeg,png}: image encoding; default jpeg.
• --citation TEXT, --homepage TEXT: optional provenance metadata.
• --overall-description TEXT, --description TEXT: optional dataset descriptions; keep the distinction if downstream catalog tooling uses both.
• --enable-beam: opt into Beam; absent means direct generation.
• --beam-run-mode {multi_threading,multi_processing}: Beam DirectRunner mode; evidence default is multi_processing.
• --beam-num-workers N: Beam DirectRunner worker count; evidence default is 5.

Validate paths, version syntax, encoding choice, and positive worker count before invoking the exporter. --task-name is metadata, not a filter for source task strings; use a distinct name when the output is a differently scoped dataset.

Conversion procedure

1. Plan: freeze source metadata, output path, schema decisions, metadata fields, and runner mode. Note whether a retry may reuse partial output.
2. Build the schema: map supported LeRobot observation image, depth, state, action, and task fields as described in data-formats.md. Reject unhandled feature kinds instead of silently dropping them.
3. Normalize each step: convert ordinary images to uint8 HWC from LeRobot CHW values in [0, 1]; squeeze only a documented singleton depth channel; preserve state/action numeric dtype and shape; map task text to language_instruction.
4. Group episodes: use source episode identity and metadata lengths. Emit is_first on the first frame, and set both is_last and is_terminal on the final frame only. is_terminal here means “episode ended at the last available frame,” not a separately observed environment termination signal.
5. Write: use TFDS/RLDS metadata and TFRecord output in a new destination. Direct mode should be the first smoke path. Inspect counts and representative shapes before considering a larger run.
6. Review: verify the output split, episode count, step counts, metadata, image encoding, depth rank, and boundary flags. Only then publish or hand the directory to downstream tooling.

Runner choice and recovery

• Direct runner: deterministic episode traversal, no Beam dependency, and the recommended path for small datasets. It is easier to diagnose because the regular generator walks the LeRobot dataset once and closes each episode when the episode index changes.
• Beam DirectRunner: optional parallel episode processing using multi_processing or multi_threading and a bounded worker count. Use only after a successful direct smoke test and when throughput justifies it.
• The evidence workflow warns that Beam can lose episodes while sharding or saving. Treat any Beam run as potentially incomplete: compare episode ids and counts, and rerun in direct mode if they differ. multi_threading has an additional sharding/saving warning; prefer multi_processing when Beam is approved.
• A failed or partial run is not a valid dataset. Move it aside, remove it only after confirming it is the intended output, and rerun from a clean destination. Do not “repair” missing episodes by editing boundary flags.

Safe verification

Before a real export, perform only safe checks:

• Help-only: invoke the equivalent CLI with --help; confirm all listed flags and defaults without opening a dataset or creating output.
• Import-only: import TensorFlow, TFDS, and the selected LeRobot metadata API; import Apache Beam only when Beam is selected. Do not download datasets or instantiate a conversion builder during this check.
• Synthetic mapping: feed a tiny in-memory feature map and one or two synthetic step records to a mapping implementation. Assert image CHW→HWC and [0,1]→uint8, singleton depth behavior, state/action key mapping, task to language_instruction, and exactly one first/last/terminal marker per episode.
• No native conversion: do not run a real conversion, Beam job, download, Hub push, or large write as part of skill drafting or help verification.

Source basis: the Any4LeRobot LeRobot-to-RLDS README and its exporter module. The source module is adapted into this contract and references only; no source file, checkout path, or source script is a runtime dependency.