I4h Workflow Dataset Convert
NVIDIAOfficialCreated by Isaac for Healthcare TeamDistributed by NVIDIA
- Skill
- Workflow
- Data and AI
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured procedure for converting HDF5 recordings from the Isaac for Healthcare (i4h) framework into the LeRobot dataset format. It handles the extraction of state, actions, camera data, and task instructions, ensuring the resulting dataset is compatible with downstream training pipelines. The process includes environment resolution, data conversion, and a verification step to ensure metadata, modality files, and episode counts are correct.
What it does
- Resolves workflow, scene, and embodiment metadata from HDF5 recordings.
- Converts successful episodes from HDF5 format to LeRobot-compatible datasets.
- Generates required metadata files including stats and modality mappings.
- Supports specific action mapping for G1 robot configurations.
- Verifies the integrity of the converted dataset, including parquet data and video files.
- Quarantines incomplete or failed conversion attempts.
When to use it
- When preparing recorded robot workflow data for training machine learning policies.
- When you need to inspect recorded robot actions and states using LeRobot tools.
- When migrating legacy G1 recordings to modern WBC policy formats.
- When you need to ensure dataset compatibility with GR00T policy loaders.
Advantages
- Ensures consistency between source recordings and training-ready datasets.
- Automates the generation of essential metadata like modality and stats.
- Provides built-in verification to catch missing data or dimension mismatches.
- Handles complex action-space mapping for specific robot embodiments.
Usage examples
- “Convert my scissor pick-and-place recording into a LeRobot dataset.”
- The agent resolves the workflow and embodiment, converts successful episodes, and verifies the output metadata, parquet files, and camera videos.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/i4h-workflow-dataset-convert/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip i4h-workflow-dataset-convert.zip -d .agents/skills
Documentation by Isaac for Healthcare Team
Convert Workflow HDF5 to LeRobot
Purpose
Preserve recorded actions, state, cameras, task text, and embodiment labels in a local LeRobot dataset.
Instructions
Run the checkout resolver and select the source HDF5.
Read the workflow, Scene, embodiment, and instruction.
Run conversion for the selected successful episodes.
Inspect metadata, parquet, videos, and feature widths.
Resolve and inspect
export I4H_WORKFLOWS_REPO_URL="${I4H_WORKFLOWS_REPO_URL:-https://github.com/isaac-for-healthcare/i4h-workflows}"
I4H_REPO_DIR_NAME="${I4H_WORKFLOWS_REPO_URL%/}"
I4H_REPO_DIR_NAME="${I4H_REPO_DIR_NAME##*/}"
I4H_REPO_DIR_NAME="${I4H_REPO_DIR_NAME##*:}"
I4H_REPO_DIR_NAME="${I4H_REPO_DIR_NAME%.git}"
[ -n "$I4H_REPO_DIR_NAME" ] || { echo "Cannot derive a checkout name from I4H_WORKFLOWS_REPO_URL" >&2; exit 2; }
ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"
if [ ! -d "$ROOT/workflows/i4h_workflows" ]; then
ROOT="${I4H_WORKFLOWS:-$HOME/$I4H_REPO_DIR_NAME}"
[ -d "$ROOT/workflows/i4h_workflows" ] || git clone "$I4H_WORKFLOWS_REPO_URL" "$ROOT"
fi
export I4H_WORKFLOWS="$ROOT"
cd "$ROOT"
HDF5_PATH=/absolute/path/to/recording.hdf5
uv run --project tools/dataset i4h-dataset inspect "$HDF5_PATH" --segmentsTreat the resolver above as part of the skill contract: a hosted copy may run outside the base repository, so never assume the current checkout contains workflows/i4h_workflows. I4H_WORKFLOWS_REPO_URL selects the clone source. When I4H_WORKFLOWS is unset, derive the fallback directory from that URL; set I4H_WORKFLOWS only to reuse or choose a specific destination. Never replace an existing checkout.
Use the explicit/current-chain recording. Resolve its workflow and Scene from recording metadata/context, then read the Scene manifest for the embodiment and instruction. Use the embodiment manifest for labels. Do not assume state width equals action width; the converter derives both from the recording.
Convert
RUN_DIR="$(pwd)/runs/<workflow>/$(date +%Y%m%d_%H%M%S)"
DATASET_DIR="$RUN_DIR/lerobot/local/<name>"
mkdir -p "$(dirname "$DATASET_DIR")"
[ ! -e "$DATASET_DIR" ] || { echo "Destination already exists: $DATASET_DIR" >&2; exit 2; }
uv run --project tools/dataset i4h-dataset convert \
"$HDF5_PATH" "$DATASET_DIR" \
--robot <embodiment> \
--repo-id "local/<name>" \
--successful-only \
--task "<instruction>"Use --fps or --skip-frames only when the user requests it or source metadata justifies it. Keep the default H.264 video codec for compatibility with GR00T's fast decord loader; select another --video-codec only when the target consumer requires it.
Conversion writes aggregate meta/stats.json for downstream policy loaders. Native G1 rule-based WBC recordings already contain 43-D state and 50-D action; the converter recognizes that contract and writes GR00T's required semantic meta/modality.json automatically. For a G1 recording made through the legacy 23-D Pink/keyboard contract and destined for a 50-D G1 WBC policy Task, add --g1-wbc-policy-actions. That explicit mapping combines the measured 43-joint state with the recorded navigation, base-height, and torso commands; require source action width 23 and state width 43.
Verify
Require:
meta/info.jsonmeta/stats.jsonmeta/modality.jsonwhen the target trainer requires semantic modality slicesepisode parquet data
video files for every recorded camera
converted episode count matching selected successful sources
action/state feature widths and names matching the recording plus embodiment descriptor
For G1, require modality metadata for both supported paths: native state=43/action=50, or explicitly mapped state=43/source-action=23/output-action=50. Treat a native 50-D dataset without meta/modality.json as incomplete.
Treat missing inputs or zero converted episodes as failure. If conversion leaves a partial destination, quarantine or remove that exact incomplete directory before retrying; never report it as usable.
Troubleshooting
On dimension errors, resolve the source workflow and embodiment again. On missing videos, confirm frames existed before conversion.
Prerequisites
Require a readable HDF5 recording and its matching Scene plus embodiment manifests.
Limitations
Conversion cannot reconstruct missing cameras, actions, state, task text, or successful episodes.
Examples
Convert my scissor pick-and-place recording into a LeRobot dataset.→ resolveso101, convert successful episodes, and verify metadata, parquet, and both camera videos.
Completion gate
Report source HDF5/workflow, embodiment, task text, source/converted/skipped counts, action/state widths, output directory/repo id, aggregate-stats/modality/parquet/video checks, and any missing modality.
Files
- ATTRIBUTION.mdAdded by AutoGineer961 B
- BENCHMARK.md7.0 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md5.2 KiB
- evals/evals.json2.3 KiB
- skill-card.md4.0 KiB
- skill.oms.sig4.5 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
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Attribution and licence
- Created by
- Isaac for Healthcare Team
- Publisher
- NVIDIA (Official)
- Distributed by
- NVIDIA
- Source
- NVIDIA/skills · skills/i4h-workflow-dataset-convert · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Isaac for Healthcare Team or NVIDIA.
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