mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-11 14:49:43 +00:00
Jade Choghari
473f1bd0e0
* add assets * add libero results pifast: * update * update * update size * update naems: : * update training tokenizer
102 lines
5.0 KiB
Plaintext
102 lines
5.0 KiB
Plaintext
# 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]"
|
|
```
|
|
|
|
> [!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 \
|
|
--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).
|