Asset Management

Datasets, checkpoints, robot models, and demo media are not tracked in Git. They are distributed via ModelScope and HuggingFace. The canonical Unitree G1 model is downloaded to assets/robots/unitree_g1/g1_29dof.xml.

What's Not in Git

• assets/robots/ - Canonical robot XML/meshes
• teleopit/retargeting/gmr/assets/ - GMR retargeting assets, IK configs, and non-canonical robot descriptions
• data/, checkpoints, caches
• Demo media (assets/demo.gif, assets/demo.mp4)

Repositories

ModelScope (default download source)

Repository	Type	Contents
BingqianWu/Teleopit-models	model	Checkpoints, GMR retargeting assets, sample BVH
BingqianWu/Teleopit-datasets	dataset	Training/validation datasets

HuggingFace (alternative)

Repository	Type	Contents
12e21/Teleopit-models	model	Checkpoints, GMR retargeting assets, sample BVH
12e21/Teleopit-datasets	dataset	Training/validation datasets

Asset Group Mapping

Group	Repository	Remote Path
ckpt	Teleopit-models	checkpoints/track.onnx, checkpoints/track.pt
robots	Teleopit-models	archives/robot_assets.tar.gz
gmr	Teleopit-models	archives/gmr_assets.tar.gz
bvh	Teleopit-models	archives/sample_bvh.tar.gz
data	Teleopit-datasets	data/datasets/*/*.h5 (lafan1, pico_record, seed, twist2)

Download

Use the project download script (defaults to ModelScope):

# Download everything
python scripts/setup/download_assets.py

# Only inference essentials
python scripts/setup/download_assets.py --only robots gmr ckpt bvh

# Only training data
python scripts/setup/download_assets.py --only data

# Download from HuggingFace instead
python scripts/setup/download_assets.py --source huggingface

Local paths after download:

Remote	Local
checkpoints/track.onnx	track.onnx
checkpoints/track.pt	track.pt
archives/robot_assets.tar.gz	assets/robots/ (extracted)
archives/gmr_assets.tar.gz	teleopit/retargeting/gmr/assets/ (extracted)
archives/sample_bvh.tar.gz	data/sample_bvh/ (extracted)
data/datasets/*/*.h5	data/datasets/

Upload to ModelScope

Step 1: Prepare Upload Directory

python scripts/setup/prepare_modelscope_assets.py --only ckpt robots gmr bvh --clean
python scripts/setup/prepare_modelscope_assets.py --only data

Output goes to data/modelscope_upload/.

Step 2: Upload

# Model repo
modelscope upload --repo-type model BingqianWu/Teleopit-models \
    data/modelscope_upload/checkpoints checkpoints
modelscope upload --repo-type model BingqianWu/Teleopit-models \
    data/modelscope_upload/archives archives

# Dataset repo
modelscope upload --repo-type dataset BingqianWu/Teleopit-datasets \
    data/modelscope_upload/data data

Step 3: Tag Version

Only the model repo supports tags (dataset repo does not).

python - <<'EOF'
from modelscope.hub.api import HubApi
api = HubApi()
url = api.create_model_tag("BingqianWu/Teleopit-models", "vX.Y.Z")
print(url)
EOF

Tags should match Git tags for traceability.

Upload to HuggingFace

Step 1: Prepare and Upload

# Prepare and upload model assets (--clean ensures no leftover files)
python scripts/setup/upload_hf_assets.py --only ckpt robots gmr bvh --clean

# Prepare and upload dataset
python scripts/setup/upload_hf_assets.py --only data --clean

Use --dry-run to stage files locally without uploading.

warning

Always use --clean when running --only, otherwise the staging directory may carry leftover files from a previous run, causing unintended uploads.

Step 2: Tag Version

python - <<'EOF'
from huggingface_hub import HfApi
api = HfApi()
api.create_tag("12e21/Teleopit-models", tag="vX.Y.Z", repo_type="model")
EOF

Pre-Push Check

python scripts/dev/check_large_tracked_files.py

This blocks large binary files and checks tracked file size limits.