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Pepijn
113 lines
5.0 KiB
Plaintext
113 lines
5.0 KiB
Plaintext
# π₀ (Pi0)
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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.
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## Model Overview
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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 robots 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.
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### The Vision for Physical Intelligence
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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.
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### Architecture and Approach
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π₀ combines several key innovations:
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- **Flow Matching**: Uses a novel method to augment pre-trained VLMs with continuous action outputs via flow matching (a variant of diffusion models)
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- **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
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- **Internet-Scale Pre-training**: Inherits semantic knowledge from a pre-trained 3B parameter Vision-Language Model
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- **High-Frequency Control**: Outputs motor commands at up to 50 Hz for real-time dexterous manipulation
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## Installation Requirements
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⚠️ **Warning**: This policy requires patching the Hugging Face `transformers` library.
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### Prerequisites
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1. Ensure you have the exact version installed:
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```bash
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pip show transformers
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```
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It must be version **4.53.2**.
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2. Apply the custom patches:
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```bash
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cp -r ./src/lerobot/policies/pi0/transformers_replace/* \
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$(python -c "import transformers, os; print(os.path.dirname(transformers.__file__))")
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```
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### What the patches do:
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- Support the **AdaRMS optimizer**
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- Correctly control the precision of activations
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- Allow the KV cache to be used without updates
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**Important Notes:**
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- This permanently modifies your `transformers` installation
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- The changes survive reinstalls unless you explicitly remove the patched files or recreate the environment
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### Restoring Clean State
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To undo the patches and restore a clean state:
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```bash
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pip uninstall transformers
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pip install transformers==4.53.2
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```
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## Training Data and Capabilities
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π₀ is trained on the largest robot interaction dataset to date, combining three key data sources:
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1. **Internet-Scale Pre-training**: Vision-language data from the web for semantic understanding
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2. **Open X-Embodiment Dataset**: Open-source robot manipulation datasets
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3. **Physical Intelligence Dataset**: Large and diverse dataset of dexterous tasks across 8 distinct robots
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## Usage
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To use π₀ in LeRobot, specify the policy type as:
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```python
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policy.type=pi0
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```
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## Training
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For training π₀, you can use the standard LeRobot training script with the appropriate configuration:
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```bash
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python src/lerobot/scripts/train.py \
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--dataset.repo_id=your_dataset \
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--policy.type=pi0 \
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--output_dir=./outputs/pi0_training \
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--job_name=pi0_training \
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--policy.pretrained_path=pepijn223/pi0_base_fp32 \
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--policy.repo_id=your_repo_id \
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--policy.compile_model=true \
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--policy.gradient_checkpointing=true \
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--policy.dtype=bfloat16 \
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--steps=3000 \
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--policy.scheduler_decay_steps=3000 \
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--policy.device=cuda \
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--batch_size=32
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```
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### Key Training Parameters
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- **`--policy.compile_model=true`**: Enables model compilation for faster training
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- **`--policy.gradient_checkpointing=true`**: Reduces memory usage significantly during training
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- **`--policy.dtype=bfloat16`**: Use mixed precision training for efficiency
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- **`--batch_size=32`**: Batch size for training, adapt this based on your GPU memory
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- **`--policy.pretrained_path=pepijn223/pi0_base_fp32`**: The base π₀ model you want to finetune, options are:
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- [pepijn223/pi0_base_fp32](https://huggingface.co/pepijn223/pi0_base_fp32)
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- [pepijn223/pi0_libero_fp32](https://huggingface.co/pepijn223/pi0_libero_fp32) (specifically trained on the Libero dataset)
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- [pepijn223/pi0_droid_fp32](https://huggingface.co/pepijn223/pi0_droid_fp32) (specifically trained on the Droid dataset)
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## License
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This model follows the **Apache 2.0 License**, consistent with the original [OpenPI repository](https://github.com/Physical-Intelligence/openpi).
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