Challenge Download Datasets

challenge-download-datasets — Fetch LeRobot v2.1 training data

Download the official LeRobot v2.1 training datasets for the Simulation Challenge from ModelScope (agibot_world/GenieSim3.0-Dataset) via the bundled download_dataset.sh. The data is organized into task suites; pick one or grab all of them.

Self-contained: the downloader ships with this skill at scripts/download_dataset.sh — you don't need the genie-sim repo checked out. The repo also has it at ./scripts/download_dataset.sh; either works. Examples below use $SKILL_DIR/scripts/download_dataset.sh where $SKILL_DIR is this skill's directory.

This produces the training corpus consumed when training/finetuning a contestant model — once you have a checkpoint, hand off to challenge-baseline-model (provision/run inference) and the rest of the challenge-help pipeline.

Prerequisite — modelscope CLI

The script shells out to the modelscope downloader. Install it once if missing:

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command -v modelscope >/dev/null || pip install modelscope

Available task suites

Each suite is downloaded in LeRobot v2.1 format.

Usage

Signature: download_dataset.sh [SUITE_NAME] [LOCAL_DIR]. Resolve the bundled script path first (works regardless of cwd), then call it:

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SCRIPT="$(dirname "$0")/scripts/download_dataset.sh"   # or hard-code this skill's scripts/ path

# Download ONE suite to ./data/<suite>/  (default LOCAL_DIR is ./data/)
"$SCRIPT" instruction

# Download a suite to a custom base dir → /path/to/save/sim2real/
"$SCRIPT" sim2real /path/to/save

# Download ALL suites (instruction + manipulation + sim2real)
"$SCRIPT"

# Help
"$SCRIPT" -h

LOCAL_DIR is the base dir — output lands at <LOCAL_DIR>/<suite>/, so it doesn't matter which directory you invoke the script from.

Output layout: a suite lands at <LOCAL_DIR>/<suite>/ (default ./data/<suite>/). The script downloads to a temp dir first, then copies task_suite/<suite>/ contents into the target — so the task_suite/ prefix is stripped in the final layout.

Notes

• Pick a suite to limit size — omitting SUITE_NAME downloads all three, which is large. Prefer naming the specific suite the user needs.
• Resumable: modelscope download caches/resumes; re-running after an interruption continues rather than restarting from scratch.
• Invalid suite names fail fast — the script only accepts instruction, manipulation, sim2real; anything else exits with the valid list.
• Disk + network: downloads can be tens of GB. Run with run_in_background if driving from the assistant so the session stays responsive, and confirm the target disk has room first.
• After data is in place, training is out of scope for this skill — see challenge-baseline-model to stand up inference once you have a checkpoint, and challenge-help for the full job pipeline.