https://github.com/lucidrains/pi-zero-pytorch
Implementation of π₀, the robotic foundation model architecture proposed by Physical Intelligence
https://github.com/lucidrains/pi-zero-pytorch
artificial-intelligence deep-learning flow-matching flow-policy robotics transformers
Last synced: 6 months ago
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Implementation of π₀, the robotic foundation model architecture proposed by Physical Intelligence
- Host: GitHub
- URL: https://github.com/lucidrains/pi-zero-pytorch
- Owner: lucidrains
- License: mit
- Created: 2024-11-01T16:42:49.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-08T16:53:40.000Z (about 1 year ago)
- Last Synced: 2024-11-08T17:41:43.073Z (about 1 year ago)
- Topics: artificial-intelligence, deep-learning, flow-matching, flow-policy, robotics, transformers
- Language: Python
- Homepage:
- Size: 1.13 MB
- Stars: 70
- Watchers: 8
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-foundation-model-ros - pi-zero-pytorch - pytorch) (Research-Grade Frameworks)
README
## pi-zero-pytorch (wip)
Implementation of π₀ the robotic foundation model architecture proposed by Physical Intelligence
Summary of this work would be that it is a simplified Transfusion (Zhou et al.) with influence from Stable Diffusion 3 (Esser et al.), mainly the adoption of flow matching instead of diffusion for policy generation, as well as the separation of parameters (Joint Attention from mmDIT). They build on top of a pretrained vision language model, PaliGemma 2B.
Update: The [official repository](https://github.com/Physical-Intelligence/openpi) has been open sourced!
### Appreciation
- [Einops](https://github.com/arogozhnikov/einops) for the amazing [pack and unpack](https://einops.rocks/4-pack-and-unpack/), used extensively here for managing various token sets
- [Flex Attention](https://pytorch.org/blog/flexattention/) for allowing for easy mixture of autoregressive and bidirectional attention
- [@Wonder1905](https://github.com/Wonder1905) for the code review and identifying issues
- You? maybe a phd student who wants to contribute to the latest SOTA architecture for behavioral cloning?
### Install
```bash
$ pip install pi-zero-pytorch
```
### Usage
```python
import torch
from pi_zero_pytorch import π0
model = π0(
dim = 512,
dim_action_input = 6,
dim_joint_state = 12,
num_tokens = 20_000
)
vision = torch.randn(1, 1024, 512)
commands = torch.randint(0, 20_000, (1, 1024))
joint_state = torch.randn(1, 12)
actions = torch.randn(1, 32, 6)
loss, _ = model(vision, commands, joint_state, actions)
loss.backward()
# after much training
sampled_actions = model(vision, commands, joint_state, trajectory_length = 32) # (1, 32, 6)
```
To do online learning, just wrap the model with the `Agent` class
```python
from pi_zero_pytorch import π0, Agent, EPO
# wrap the model with `Agent`, which will instantiate actor and critic for PPO
agent = Agent(model)
# you'll want to supply your own environment
from pi_zero_pytorch.mock_env import Env
mock_env = Env((256, 256), 2, 32, 1024, 12)
# pass your agent and environment to EPO for learning to be orchestrated
epo = EPO(agent, mock_env)
# gather memories from environment
memories = epo.gather_experience_from_env(steps = 10)
# learn from memories
epo.learn_agent(memories, batch_size = 2)
```
### Contributing
At the project root, run
```bash
$ pip install '.[test]' # or `uv pip install '.[test]'`
```
Then add your tests to `tests/test_pi_zero.py` and run
```bash
$ pytest tests/
```
That's it
### Citation
```bibtex
@misc{Black2024,
author = {Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Lucy Xiaoyang Shi, James Tanner, Quan Vuong, Anna Walling, Haohuan Wang, Ury Zhilinsky},
url = {https://www.physicalintelligence.company/download/pi0.pdf}
}
```
```bibtex
@inproceedings{Zhou2024ValueRL,
title = {Value Residual Learning For Alleviating Attention Concentration In Transformers},
author = {Zhanchao Zhou and Tianyi Wu and Zhiyun Jiang and Zhenzhong Lan},
year = {2024},
url = {https://api.semanticscholar.org/CorpusID:273532030}
}
```
```bibtex
@inproceedings{Darcet2023VisionTN,
title = {Vision Transformers Need Registers},
author = {Timoth'ee Darcet and Maxime Oquab and Julien Mairal and Piotr Bojanowski},
year = {2023},
url = {https://api.semanticscholar.org/CorpusID:263134283}
}
```
```bibtex
@article{Li2024ImmiscibleDA,
title = {Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment},
author = {Yiheng Li and Heyang Jiang and Akio Kodaira and Masayoshi Tomizuka and Kurt Keutzer and Chenfeng Xu},
journal = {ArXiv},
year = {2024},
volume = {abs/2406.12303},
url = {https://api.semanticscholar.org/CorpusID:270562607}
}
```
```bibtex
@inproceedings{Sadat2024EliminatingOA,
title = {Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion Models},
author = {Seyedmorteza Sadat and Otmar Hilliges and Romann M. Weber},
year = {2024},
url = {https://api.semanticscholar.org/CorpusID:273098845}
}
```
```bibtex
@article{Bulatov2022RecurrentMT,
title = {Recurrent Memory Transformer},
author = {Aydar Bulatov and Yuri Kuratov and Mikhail S. Burtsev},
journal = {ArXiv},
year = {2022},
volume = {abs/2207.06881},
url = {https://api.semanticscholar.org/CorpusID:250526424}
}
```
```bibtex
@inproceedings{Bessonov2023RecurrentAT,
title = {Recurrent Action Transformer with Memory},
author = {A. B. Bessonov and Alexey Staroverov and Huzhenyu Zhang and Alexey K. Kovalev and D. Yudin and Aleksandr I. Panov},
year = {2023},
url = {https://api.semanticscholar.org/CorpusID:259188030}
}
```
```bibtex
@article{Zhu2024HyperConnections,
title = {Hyper-Connections},
author = {Defa Zhu and Hongzhi Huang and Zihao Huang and Yutao Zeng and Yunyao Mao and Banggu Wu and Qiyang Min and Xun Zhou},
journal = {ArXiv},
year = {2024},
volume = {abs/2409.19606},
url = {https://api.semanticscholar.org/CorpusID:272987528}
}
```
```bibtex
@inproceedings{Sun2025F5RTTSIF,
title = {F5R-TTS: Improving Flow-Matching based Text-to-Speech with Group Relative Policy Optimization},
author = {Xiaohui Sun and Ruitong Xiao and Jianye Mo and Bowen Wu and Qun Yu and Baoxun Wang},
year = {2025},
url = {https://api.semanticscholar.org/CorpusID:277510064}
}
```
```bibtex
@inproceedings{Wang2025EvolutionaryPO,
title = {Evolutionary Policy Optimization},
author = {Jianren Wang and Yifan Su and Abhinav Gupta and Deepak Pathak},
year = {2025},
url = {https://api.semanticscholar.org/CorpusID:277313729}
}
```
[*dear alice*](https://www.youtube.com/watch?v=z-Ng5ZvrDm4)