---
license: mit
base_model: openvla/openvla-7b
library_name: peft
tags: [openvla, oft, lora, libero, robotics, vision-language-action]
---

# HiF-VLA — LIBERO per-suite LoRA experts

Per-suite OpenVLA-OFT LoRA adapters (plus `action_head` / `motion_*` heads) extracted
from the published HiF-VLA checkpoints. The OpenVLA-7B base weights are **not** included.

| Suite | Source repo | LoRA r/alpha | closed-loop success (measured) |
|---|---|---|---|
| spatial | `minnielin/hifvla-libero-spatial` | 32 / 16 | 100% (10 ep) |
| object  | `minnielin/hifvla-libero-object`  | 32 / 16 | 96% (50 ep) |
| goal    | `minnielin/hifvla-libero-goal`    | 32 / 16 | 100% (50 ep) |
| long    | `minnielin/hifvla-libero-long`    | 32 / 16 | 96% (50 ep) |

- Base model: `openvla/openvla-7b`
- Method: HiF-VLA (Hindsight/Insight/Foresight motion representation), CVPR 2026, arXiv 2512.09928
- Upstream code: https://github.com/minnie-lin/HiF-VLA

Each `<suite>/` contains `lora_adapter/` (adapter_config.json + adapter_model.safetensors),
`action_head--checkpoint.pt`, `motion_encoder--checkpoint.pt`, `motion_manager--checkpoint.pt`,
`proprio_projector--checkpoint.pt`, config/tokenizer files.

## Reproduce the evaluation

Needs the HiF-VLA repo + LIBERO + a moojink OpenVLA-OFT / transformers-fork environment,
plus `ffmpeg` (the eval uses it for motion-vector extraction; `imageio-ffmpeg`'s bundled
binary works) and `av`. `mvextractor` is training-only; guard its import.

```bash
python experiments/robot/libero/run_libero_eval.py \
  --use_proprio True --num_images_in_input 2 --use_film False \
  --pretrained_checkpoint <suite checkpoints merged with base> \
  --task_suite_name libero_<suite> --history_length 8 --unnorm_key libero_<suite>_no_noops
```