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# 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.
### 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]"
```
> [!NOTE]
> For lerobot 0.4.0, if you want to install pi tag, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
>
> This will be solved in the next patch release
## 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
python src/lerobot/scripts/lerobot_train.py \
--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 \
--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)
## License
This model follows the **Apache 2.0 License**, consistent with the original [OpenPI repository](https://github.com/Physical-Intelligence/openpi).
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