| # 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: |
|
|
| ```bash |
| 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): |
|
|
| ```bash |
| 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. |
| |
