---
language: en
license: mit
base_model: openvla/openvla-7b
base_model_relation: adapter
tags:
  - robotics
  - vision-language-action
  - residual-adapter
  - bas-vla
  - arxiv:2609.23650
---

# BAS-VLA: OpenVLA residual adapter

[Paper](https://arxiv.org/abs/2609.23650) · [Project](https://nebulis-lab.com/BAS-VLA/) · [Code](https://github.com/NEBULIS-Lab/BAS-VLA)

This release provides a trained text-conditioned residual adapter and matched inputs for the **OpenVLA / LIBERO-10 offline action-calibration setting**, a selected subset of the settings studied in *Beyond Appearance Shifts: Task-Semantic Action Calibration for VLA Models*.

Each record pairs a shared observation with clean, task-preserving, and semantics-changing instructions and their frozen-carrier actions. The included cases cover target-object and executability-condition changes. The adapter applies a bounded action correction using the base action and binary bag-of-words instruction features.

## Files

| Location | Contents |
| --- | --- |
| `adapter/` | Trained residual weights, vocabulary, and loading/action metadata |
| `data/triplets.json` | Matched action triplets, expert actions, and demonstration-level train/validation/test assignments |
| `data/observations.npz` | Source agent-view RGB images aligned with the records |
| `data/states/` | Sampled simulator states and source demonstration initial states |
| `data/scenes/` | The corresponding LIBERO BDDL scenes |
| `data/cases.json`, `data/manifest.json` | Instruction triplets, source references, array alignment, and input preprocessing |
| `normalization/` | Action statistics used to construct the frozen OpenVLA actions and their source information |
| `training_config.json`, `offline_metrics.json` | Training objective configuration and offline evaluation summaries |
| `example.py` | CPU example applying the adapter to a supplied record |

## Download and use

Install the [BAS-VLA code](https://github.com/NEBULIS-Lab/BAS-VLA) and its dependencies, then download the files:

```python
from huggingface_hub import snapshot_download

snapshot_download("Shuaijun/BAS-VLA", local_dir="artifacts/bas-vla")
```

Run the included example from the directory containing `artifacts/`:

```bash
python artifacts/bas-vla/example.py
```

The example loads the adapter on CPU and prints calibrated clean, control, and changed-instruction actions for a held-out record. Use `--record-index` to select another record.

## Action and feature conventions

Use the exact contract in `adapter/adapter_metadata.json` when supplying new actions. The carrier is `openvla/openvla-7b`; the cached actions use the bundled `libero_10_no_noops` statistics from `moojink/openvla-7b-oft-finetuned-libero-10`, followed by the documented gripper transform. Instructions use the supplied binary bag-of-words vocabulary.

The checkpoint's action-space identifier is `openvla_libero10_unnormalized_sign_gripper_offline`. The code repository's OpenPI and OpenVLA-OFT rollout runners require adapters matched to their own carrier and action conventions. Load this checkpoint through the supplied example or the `ResidualAdapter` class with the recorded convention.

`data/manifest.json` explains how RGB arrays, simulator states, scene files, and demonstration frames align. LIBERO supplies the scene assets and simulator. Base VLA weights are obtained separately from their upstream model repositories.

## Sources and license

BAS-VLA contributions are released under the MIT License. The included LIBERO-derived data and OpenVLA-OFT normalization statistics retain their upstream MIT notices. See [NOTICE.md](NOTICE.md) and `licenses/` for the source links and notices.

## Citation

```bibtex
@inproceedings{liu2026basvla,
  title = {Beyond Appearance Shifts: Task-Semantic Action Calibration for VLA Models},
  author = {Liu, Shuaijun and You, Feiyang and Wu, Chengyu and Hao, Shuyang and Zhang, Chenglong and Cai, Jingyao and Chen, Xingwei and Sun, Li and Su, Ningxin},
  booktitle = {Advances in Neural Information Processing Systems},
  year = {2026},
  url = {https://arxiv.org/abs/2609.23650}
}
```