---
license: apache-2.0
tags:
  - robotics
  - vla
  - openpi
  - pi0.5
  - franka
library_name: openpi
---

# pi0.5 fine-tuned on `zhuoKCL/prgvla_stack`

Single-task pi0.5 (JAX) fine-tune for **cup stacking** on a Franka.
Trained from `gs://openpi-assets/checkpoints/pi05_droid/params` using `openpi`'s `pi05_droid_finetune` recipe with our own re-computed norm stats.

- Step: **55 000** (last fully finalized checkpoint; the 60 000-step run got I/O-blocked while saving step 58 000, but loss had already plateaued at ~0.001 from ~step 50 000 onwards, so 55 000 is functionally the converged model)
- Final loss: ~0.001 (flow-matching MSE)
- Action: `(horizon=16, dim=32)` — pi0.5 standard
- State: 8-dim Franka (joint_position 7 + gripper 1)
- Cameras (from DROID layout):
  - `base_0_rgb` ← `ext_1`
  - `left_wrist_0_rgb` ← `wrist`
  - `right_wrist_0_rgb` ← zeros (mask=False)
- Prompt: per-episode natural-language synonym from `tasks.jsonl` (NO fixed phrase)

## Files

| Path | Purpose |
|---|---|
| `params/` | orbax checkpoint, JAX params (12 GB) |
| `assets/zhuoKCL/prgvla_stack/norm_stats.json` | q01/q99 quantile norm stats (pi05 standard) |
| `norm_stats.json` | same file copied to root for quick inspection |

`train_state/` (optimizer state, ~30 GB) is **not** included — inference does not need it.

## Use it from `openpi`

In your local copy of `openpi`, edit `src/openpi/training/config.py` → the `pi05_droid_finetune` entry:

```python
TrainConfig(
    name="pi05_droid_finetune",
    model=pi0_config.Pi0Config(pi05=True, action_dim=32, action_horizon=16),
    data=LeRobotDROIDDataConfig(
        repo_id="zhuoKCL/prgvla_stack",            # 1) was: lerobot's droid repo
        base_config=DataConfig(prompt_from_task=True),
        # 2) remove / comment out any AssetsConfig(asset_id="droid", ...) line
    ),
    weight_loader=weight_loaders.CheckpointWeightLoader(
        "<path-to-this-repo>/params"               # or huggingface-cli download
    ),
    num_train_steps=60_000,
),
```

Then run inference exactly as upstream does.

## Inference contract (observation dict)

```python
obs = {
    "observation/joint_position": np.ndarray(7,),
    "observation/gripper_position": float,                 # scalar, becomes 1d
    "observation/exterior_image_1_left": uint8 H×W×3,      # → base_0_rgb
    "observation/wrist_image_left":      uint8 H×W×3,      # → left_wrist_0_rgb
    "prompt": "<natural language sentence>",
}
```

## Training data

[`zhuoKCL/prgvla_stack`](https://huggingface.co/datasets/zhuoKCL/prgvla_stack) (LeRobot v2.1).