hardware/v1/USAGE.md

ALTER hardware v1 downloads

Code: https://github.com/labicon/ALTER/tree/a4919df35d703fafcf054717f8a53fd876b6a010 Models: https://huggingface.co/Berkeley-ICON-Lab/ALTER-models/tree/main/hardware/v1 Data: https://huggingface.co/datasets/Berkeley-ICON-Lab/ALTER-data/tree/main/hardware/v1

Use the hardware release branch and its Python 3.10 offline installation profile:

git clone https://github.com/labicon/ALTER.git
cd ALTER
git checkout a4919df35d703fafcf054717f8a53fd876b6a010

From that checkout, download and validate the model-only bundle (about 0.82 GB):

python scripts/download_release.py --manifest release/hardware-manifest.json \
  --bundles hardware-models --destination /path/to/ALTER-hardware-artifacts
python scripts/materialize_hardware_release.py \
  --bundle-root /path/to/ALTER-hardware-artifacts \
  --output "$PWD/public-validation/hardware-models" --bundles hardware-models
python scripts/validate_hardware_models.py \
  --root "$PWD/public-validation/hardware-models/hardware" \
  --output "$PWD/public-validation/hardware-model-check.json"

For adaptation training, add hardware-training-data to the downloader and materializer bundle lists (about 20.80 GB additional data). For original selected base demonstrations and replay recordings, add hardware-demonstrations-data (about 48.14 GB). Use a fresh materialization output directory for each selection. Use a separate hardware download directory from the simulation download tree; the two releases have distinct root control manifests and receipts.

The high/low training manifests are at hardware/manifests/high.json and hardware/manifests/low.json in the materialized output. See the public hardware guide for short CPU training commands, explicit base checkpoint/statistics arguments and NumPy pickle compatibility. Missing historical provenance remains explicit; no full paper reproduction or physical robot validation is claimed.

The pinned manifest seals payload checksums and export receipts. Downloads reuse valid files and reject conflicting files. Only load trusted pickle/PyTorch artifacts. Seven original models and six short training workflows passed offline checks. Physical operation requires the separate private ICON_Arm project.