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Lite-Motion-Tracking-Dataset

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LeRobot
lerobot
humanoid
motion-capture
motion-tracking
berkeley-humanoids
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Lite-Motion-Tracking-Dataset / INSTRUCTIONS.md
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Instructions

How to reproduce this dataset locally and push it to the Hugging Face Hub.

Setup 

uv sync

Generation pipeline

Four scripts under scripts/, run in order:

  1. download_lafan.py — pulls the G1 subset of lvhaidong/LAFAN1_Retargeting_Dataset into .cache/lafan1_g1/ (gitignored).
  2. retarget.py — step 1 direct G1 → Lite joint remap (sign + offset table baked into common.G1_TO_LITE) and step 2 per-frame mink IK refinement on both feet and both hands. Writes a HuggingFace LeRobotDataset at the repository root (meta/ + data/chunk-*/).
  3. visualize.py — viser web viewer; renders Lite (solid) overlaid with the source G1 (alpha-blended ghost). Episode dropdown lazy-loads each clip on selection.
  4. push_to_hub.py — uploads the dataset to the Hugging Face Hub using LeRobotDataset.push_to_hub (auto-generates the dataset card).

End-to-end reproduction:

uv run scripts/download_lafan.py
uv run scripts/retarget.py --workers -1     # parallelise across all CPU cores
uv run scripts/visualize.py                 # browse and inspect
uv run scripts/push_to_hub.py               # publish

retarget.py flags

Flag Default Effect
--clip <regex> none Process only matching CSVs (e.g. 'walk1_subject1').
--validate-only off Print step-1 vs step-2 EE-error table without writing the dataset.
--no-ik off Output step 1 only (skips per-frame IK refinement).
--ik-iters N 15 mink Newton iterations per frame in step 2.
--workers N 1 Parallel processes across clips. -1 = all CPU cores.

visualize.py flags

Flag Default Effect
--episode-index N 0 Initial episode to load (switch later from the GUI dropdown).
--port N 8080 viser HTTP port.
--no-ghost off Hide the source G1 ghost; show Lite only.

Pushing to Hugging Face Hub

Authenticate once:

hf auth login                # interactive
# or, non-interactive:
export HF_TOKEN=hf_...

Your token needs write scope on the target namespace (your account or an org you belong to).

Then upload:

uv run scripts/push_to_hub.py
uv run scripts/push_to_hub.py --repo-id your-username/your-dataset
uv run scripts/push_to_hub.py --private
uv run scripts/push_to_hub.py --branch v0.1

push_to_hub.py calls LeRobotDataset.push_to_hub, which:

  • Creates the dataset repo if it doesn't exist.
  • Uploads meta/ + data/ (and videos/ if any — there are none here).
  • Generates a LeRobot dataset card from meta/info.json (this overwrites whatever README.md was uploaded).
  • Tags the revision with the LeRobot codebase version.