# π₀ (Pi0)

π₀ is a **Vision-Language-Action model for general robot control**, from Physical Intelligence. The LeRobot implementation is adapted from their open source [OpenPI](https://github.com/Physical-Intelligence/openpi) repository.

## Model Overview

π₀ represents a breakthrough in robotics as the first general-purpose robot foundation model developed by [Physical Intelligence](https://www.physicalintelligence.company/blog/pi0). Unlike traditional robot programs that are narrow specialists programmed for repetitive motions, π₀ is designed to be a generalist policy that can understand visual inputs, interpret natural language instructions, and control a variety of different robots across diverse tasks.

<img
  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-pi0%20(1).png"
  alt="An overview of Pi0"
  width="85%"
/>

### The Vision for Physical Intelligence

As described by Physical Intelligence, while AI has achieved remarkable success in digital domains, from chess-playing to drug discovery, human intelligence still dramatically outpaces AI in the physical world. To paraphrase Moravec's paradox, winning a game of chess represents an "easy" problem for AI, but folding a shirt or cleaning up a table requires solving some of the most difficult engineering problems ever conceived. π₀ represents a first step toward developing artificial physical intelligence that enables users to simply ask robots to perform any task they want, just like they can with large language models.

### Architecture and Approach

π₀ combines several key innovations:

- **Flow Matching**: Uses a novel method to augment pre-trained VLMs with continuous action outputs via flow matching (a variant of diffusion models)
- **Cross-Embodiment Training**: Trained on data from 8 distinct robot platforms including UR5e, Bimanual UR5e, Franka, Bimanual Trossen, Bimanual ARX, Mobile Trossen, and Mobile Fibocom
- **Internet-Scale Pre-training**: Inherits semantic knowledge from a pre-trained 3B parameter Vision-Language Model
- **High-Frequency Control**: Outputs motor commands at up to 50 Hz for real-time dexterous manipulation

## Installation Requirements

1. Install LeRobot by following our [Installation Guide](./installation).
2. Install Pi0 dependencies by running:

   ```bash
   pip install -e ".[pi]"
   ```

## Training Data and Capabilities

π₀ is trained on the largest robot interaction dataset to date, combining three key data sources:

1. **Internet-Scale Pre-training**: Vision-language data from the web for semantic understanding
2. **Open X-Embodiment Dataset**: Open-source robot manipulation datasets
3. **Physical Intelligence Dataset**: Large and diverse dataset of dexterous tasks across 8 distinct robots

## Usage

To use π₀ in LeRobot, specify the policy type as:

```python
policy.type=pi0
```

## Training

For training π₀, you can use the standard LeRobot training script with the appropriate configuration:

```bash
lerobot-train \
    --dataset.repo_id=your_dataset \
    --policy.type=pi0 \
    --output_dir=./outputs/pi0_training \
    --job_name=pi0_training \
    --policy.pretrained_path=lerobot/pi0_base \
    --policy.repo_id=your_repo_id \
    --policy.compile_model=true \
    --policy.gradient_checkpointing=true \
    --policy.dtype=bfloat16 \
    --policy.freeze_vision_encoder=false \
    --policy.train_expert_only=false \
    --steps=3000 \
    --policy.device=cuda \
    --batch_size=32
```

### Key Training Parameters

- **`--policy.compile_model=true`**: Enables model compilation for faster training
- **`--policy.gradient_checkpointing=true`**: Reduces memory usage significantly during training
- **`--policy.dtype=bfloat16`**: Use mixed precision training for efficiency
- **`--batch_size=32`**: Batch size for training, adapt this based on your GPU memory
- **`--policy.pretrained_path=lerobot/pi0_base`**: The base π₀ model you want to finetune, options are:
  - [lerobot/pi0_base](https://huggingface.co/lerobot/pi0_base)
  - [lerobot/pi0_libero](https://huggingface.co/lerobot/pi0_libero) (specifically trained on the Libero dataset)

### Training Parameters Explained

| Parameter               | Default | Description                                 |
| ----------------------- | ------- | ------------------------------------------- |
| `freeze_vision_encoder` | `false` | Do not freeze the vision encoder            |
| `train_expert_only`     | `false` | Do not freeze the VLM, train all parameters |

**💡 Tip**: Setting `train_expert_only=true` freezes the VLM and trains only the action expert and projections, allowing finetuning with reduced memory usage.

## License

This model follows the **Apache 2.0 License**, consistent with the original [OpenPI repository](https://github.com/Physical-Intelligence/openpi).
