---
library_name: physicalai-train
tags:
  - vision-language-action
  - robotics
  - physicalai
license: apache-2.0
base_model:
  - lerobot/pi05_libero_finetuned_v044
---

# Pi05

That's a powerful vision-language-action model with open-world generalization capabilities.
The original implementations can be found [here](https://github.com/Physical-Intelligence/openpi).
The model takes images from one or multiple cameras, robot state, and language instruction and produces
a chunk of actions, which robot can execute as a sequence of movements in real world.

The model weights are converted from the official [HF repo](https://huggingface.co/lerobot/pi05_libero_finetuned_v044)
and can be directly applied in the [LIBERO](https://github.com/Lifelong-Robot-Learning/LIBERO) virtual benchmark environment.

To fine-tune your model with a custom dataset, you can use [Physical AI Studio](https://github.com/open-edge-platform/physical-ai-studio).

# How to Use

## Running Inference with [OpenVINO Physical AI framework](https://github.com/openvinotoolkit/physicalai).

### Installation

```bash
uv pip install physicalai numpy
```

The following API example showcases inference API for this model:

```python
import numpy as np
from physicalai.inference import InferenceModel

model = InferenceModel("pi05-libero-fp16-ov", device="CPU")

# Build a dummy LIBERO-style observation.
# LIBERO provides two cameras (agentview + wrist) and an 8-dim robot state.
# Images use the LeRobot convention: float32 in [0, 1], shape (C, H, W).
observation = {
    "images.image": np.random.rand(1, 3, 256, 256).astype(np.float32),
    "images.image2": np.random.rand(1, 3, 256, 256).astype(np.float32),
    "state": np.zeros((1, 8), dtype=np.float32),
    "task": ["sample task description"],
}

chunk = model.predict_action_chunk(observation)
```

## Running Accuracy Evaluation on LIBERO

### Installation
```bash
uv pip install physicalai-train[libero,pi05]
```

The following API example demonstrates how to run the model on LIBERO:

```python
from physicalai.benchmark import LiberoBenchmark
from physicalai.inference import InferenceModel

model = InferenceModel("pi05-libero-fp16-ov", device="CPU")

benchmark = LiberoBenchmark(
    task_suite="libero_10",
    video_dir="fail_videos"
)

results = benchmark.evaluate(model)
```

Note that the model should be downloaded and saved to the `pi05-libero-fp16-ov` folder prior to running this script.

## Legal information

The original model is distributed under [Apache 2.0](https://choosealicense.com/licenses/apache-2.0/) license. 

## Disclaimer

Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.