uom-physical-ai
/

calvin-rdvla-lora-epoch0

@@ -1,207 +1,54 @@
 ---
 base_model: openvla/openvla-7b
-library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:openvla/openvla-7b
-- lora
-- transformers
 ---
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-- PEFT 0.19.1

 ---
+license: mit
 base_model: openvla/openvla-7b
 tags:
+  - vla
+  - robotics
+  - lora
+  - calvin
+  - adversarial-robustness
 ---
+# calvin-rdvla-lora-epoch0
+**RD-VLA (OpenVLA-7B recurrent LoRA, k_iters=8) fine-tuned on CALVIN task_D_D — first post-epoch-0 checkpoint (step16151).**
+Fine-tuned with LoRA on [CALVIN task_D_D](https://github.com/mees/calvin) for the paper:
+> "Reasoning as a Double-Edged Sword: Vulnerability and Defense in VLA Pipelines"
+> NeurIPS 2026 — University of Melbourne Physical AI Group
+Code and probe results: [https://github.com/uom-physical-ai/calvin-vla-adversarial](https://github.com/uom-physical-ai/calvin-vla-adversarial)
+## Checkpoint details
+| Field | Value |
+|-------|-------|
+| Base model | `openvla/openvla-7b` |
+| Fine-tune dataset | CALVIN task_D_D (512,077 episodes) |
+| Step | 16151 |
+| Epoch (training) | 0 |
+| Adversarial probe step | 16151 |
+## Adversarial probe results (Gaussian image noise, n=300, seed=42)
+| σ | mean_RAS | p95_RAS | mean_RS |
+|---|----------|---------|---------|
+| 0.05 | 0.0158 | 0.0810 | 0.0354 |
+| 0.10 | 0.0272 | 0.1611 | 0.0629 |
+| 0.20 | 0.0673 | 0.2969 | 0.1227 |
+RAS = Relative Action Shift (L2 deviation of predicted action, normalised). Lower = more robust.
+## Usage
+```python
+from peft import PeftModel
+from transformers import AutoModelForVision2Seq, AutoProcessor
+base = AutoModelForVision2Seq.from_pretrained("openvla/openvla-7b")
+model = PeftModel.from_pretrained(base, "uom-physical-ai/calvin-rdvla-lora-epoch0")
+processor = AutoProcessor.from_pretrained("openvla/openvla-7b")
+```
+See the [GitHub repo](https://github.com/uom-physical-ai/calvin-vla-adversarial) for the full fine-tuning and probe pipeline.