| Name | pi-zero-pytorch JSON |
| Version |
0.2.5
JSON |
| download |
| home_page | None |
| Summary | π0 in Pytorch |
| upload_time | 2025-07-25 17:56:47 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License
Copyright (c) 2024 Phil Wang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
artificial intelligence
deep learning
flow policy
robotic foundation model
transformers
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<img src="./fig3.png" width="400px"></img>
## pi-zero-pytorch (wip)
Implementation of <a href="https://www.physicalintelligence.company/blog/pi0">π₀</a> the robotic foundation model architecture proposed by Physical Intelligence
Summary of this work would be that it is a simplified <a href="https://github.com/lucidrains/transfusion-pytorch">Transfusion</a> (Zhou et al.) with influence from <a href="https://arxiv.org/abs/2403.03206">Stable Diffusion 3</a> (Esser et al.), mainly the adoption of flow matching instead of diffusion for policy generation, as well as the separation of parameters (<a href="https://github.com/lucidrains/mmdit/blob/main/mmdit/mmdit_pytorch.py#L43">Joint Attention</a> 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}
}
```
```bibtex
@misc{PI2025,
title = {Real-Time Action Chunking with Large Models},
author = {Kevin Black, Manuel Y. Galliker, Sergey Levine},
year = {2025},
url = {https://www.pi.website/research/real_time_chunking}
}
```
```bibtex
@misc{PI2025,
title = {VLAs that Train Fast, Run Fast, and Generalize Better},
author = {Danny Driess, Jost Tobias Springenberg, Brian Ichter, Lili Yu, Adrian Li-Bell, Karl Pertsch, Allen Z. Ren, Homer Walke, Quan Vuong, Lucy Xiaoyang Shi, Sergey Levine},
year = {2025},
url = {https://www.physicalintelligence.company/research/knowledge_insulation}
}
```
```bibtex
@inproceedings{Wagenmaker2025SteeringYD,
title = {Steering Your Diffusion Policy with Latent Space Reinforcement Learning},
author = {Andrew Wagenmaker and Mitsuhiko Nakamoto and Yunchu Zhang and Seohong Park and Waleed Yagoub and Anusha Nagabandi and Abhishek Gupta and Sergey Levine},
year = {2025},
url = {https://api.semanticscholar.org/CorpusID:279464702}
}
```
```bibtex
@misc{dong2025reinforcementlearningimplicitimitation,
title = {Reinforcement Learning via Implicit Imitation Guidance},
author = {Perry Dong and Alec M. Lessing and Annie S. Chen and Chelsea Finn},
year = {2025},
eprint = {2506.07505},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2506.07505},
}
```
```bibtex
@misc{zhou2025efficientonlinereinforcementlearning,
title = {Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data},
author = {Zhiyuan Zhou and Andy Peng and Qiyang Li and Sergey Levine and Aviral Kumar},
year = {2025},
eprint = {2412.07762},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2412.07762},
}
```
```bibtex
@misc{cheang2025gr3technicalreport,
title = {GR-3 Technical Report},
author = {Chilam Cheang and Sijin Chen and Zhongren Cui and Yingdong Hu and Liqun Huang and Tao Kong and Hang Li and Yifeng Li and Yuxiao Liu and Xiao Ma and Hao Niu and Wenxuan Ou and Wanli Peng and Zeyu Ren and Haixin Shi and Jiawen Tian and Hongtao Wu and Xin Xiao and Yuyang Xiao and Jiafeng Xu and Yichu Yang},
year = {2025},
eprint = {2507.15493},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2507.15493},
}
```
```bibtex
@misc{heng2025vitacformerlearningcrossmodalrepresentation,
title = {ViTacFormer: Learning Cross-Modal Representation for Visuo-Tactile Dexterous Manipulation},
author = {Liang Heng and Haoran Geng and Kaifeng Zhang and Pieter Abbeel and Jitendra Malik},
year = {2025},
eprint = {2506.15953},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2506.15953},
}
```
[*dear alice*](https://www.youtube.com/watch?v=z-Ng5ZvrDm4)
Raw data
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"description": "<img src=\"./fig3.png\" width=\"400px\"></img>\n\n## pi-zero-pytorch (wip)\n\nImplementation of <a href=\"https://www.physicalintelligence.company/blog/pi0\">\u03c0\u2080</a> the robotic foundation model architecture proposed by Physical Intelligence\n\nSummary of this work would be that it is a simplified <a href=\"https://github.com/lucidrains/transfusion-pytorch\">Transfusion</a> (Zhou et al.) with influence from <a href=\"https://arxiv.org/abs/2403.03206\">Stable Diffusion 3</a> (Esser et al.), mainly the adoption of flow matching instead of diffusion for policy generation, as well as the separation of parameters (<a href=\"https://github.com/lucidrains/mmdit/blob/main/mmdit/mmdit_pytorch.py#L43\">Joint Attention</a> from mmDIT). They build on top of a pretrained vision language model, PaliGemma 2B.\n\nUpdate: The [official repository](https://github.com/Physical-Intelligence/openpi) has been open sourced!\n\n### Appreciation\n\n- [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\n\n- [Flex Attention](https://pytorch.org/blog/flexattention/) for allowing for easy mixture of autoregressive and bidirectional attention\n\n- [@Wonder1905](https://github.com/Wonder1905) for the code review and identifying issues\n\n- You? maybe a phd student who wants to contribute to the latest SOTA architecture for behavioral cloning?\n\n### Install\n\n```bash\n$ pip install pi-zero-pytorch\n```\n\n### Usage\n\n```python\nimport torch\nfrom pi_zero_pytorch import \u03c00\n\nmodel = \u03c00(\n dim = 512,\n dim_action_input = 6,\n dim_joint_state = 12,\n num_tokens = 20_000\n)\n\nvision = torch.randn(1, 1024, 512)\ncommands = torch.randint(0, 20_000, (1, 1024))\njoint_state = torch.randn(1, 12)\nactions = torch.randn(1, 32, 6)\n\nloss, _ = model(vision, commands, joint_state, actions)\nloss.backward()\n\n# after much training\n\nsampled_actions = model(vision, commands, joint_state, trajectory_length = 32) # (1, 32, 6)\n```\n\nTo do online learning, just wrap the model with the `Agent` class\n\n```python\nfrom pi_zero_pytorch import \u03c00, Agent, EPO\n\n# wrap the model with `Agent`, which will instantiate actor and critic for PPO\n\nagent = Agent(model)\n\n# you'll want to supply your own environment\n\nfrom pi_zero_pytorch.mock_env import Env\nmock_env = Env((256, 256), 2, 32, 1024, 12)\n\n# pass your agent and environment to EPO for learning to be orchestrated\n\nepo = EPO(agent, mock_env)\n\n# gather memories from environment\n\nmemories = epo.gather_experience_from_env(steps = 10)\n\n# learn from memories\n\nepo.learn_agent(memories, batch_size = 2)\n```\n\n### Contributing\n\nAt the project root, run\n\n```bash\n$ pip install '.[test]' # or `uv pip install '.[test]'`\n```\n\nThen add your tests to `tests/test_pi_zero.py` and run\n\n```bash\n$ pytest tests/\n```\n\nThat's it\n\n### Citation\n\n```bibtex\n@misc{Black2024,\n 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},\n url = {https://www.physicalintelligence.company/download/pi0.pdf}\n}\n```\n\n```bibtex\n@inproceedings{Zhou2024ValueRL,\n title = {Value Residual Learning For Alleviating Attention Concentration In Transformers},\n author = {Zhanchao Zhou and Tianyi Wu and Zhiyun Jiang and Zhenzhong Lan},\n year = {2024},\n url = {https://api.semanticscholar.org/CorpusID:273532030}\n}\n```\n\n```bibtex\n@inproceedings{Darcet2023VisionTN,\n title = {Vision Transformers Need Registers},\n author = {Timoth'ee Darcet and Maxime Oquab and Julien Mairal and Piotr Bojanowski},\n year = {2023},\n url = {https://api.semanticscholar.org/CorpusID:263134283}\n}\n```\n\n```bibtex\n@article{Li2024ImmiscibleDA,\n title = {Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment},\n author = {Yiheng Li and Heyang Jiang and Akio Kodaira and Masayoshi Tomizuka and Kurt Keutzer and Chenfeng Xu},\n journal = {ArXiv},\n year = {2024},\n volume = {abs/2406.12303},\n url = {https://api.semanticscholar.org/CorpusID:270562607}\n}\n```\n\n```bibtex\n@inproceedings{Sadat2024EliminatingOA,\n title = {Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion Models},\n author = {Seyedmorteza Sadat and Otmar Hilliges and Romann M. Weber},\n year = {2024},\n url = {https://api.semanticscholar.org/CorpusID:273098845}\n}\n```\n\n```bibtex\n@article{Bulatov2022RecurrentMT,\n title = {Recurrent Memory Transformer},\n author = {Aydar Bulatov and Yuri Kuratov and Mikhail S. Burtsev},\n journal = {ArXiv},\n year = {2022},\n volume = {abs/2207.06881},\n url = {https://api.semanticscholar.org/CorpusID:250526424}\n}\n```\n\n```bibtex\n@inproceedings{Bessonov2023RecurrentAT,\n title = {Recurrent Action Transformer with Memory},\n author = {A. B. Bessonov and Alexey Staroverov and Huzhenyu Zhang and Alexey K. Kovalev and D. Yudin and Aleksandr I. Panov},\n year = {2023},\n url = {https://api.semanticscholar.org/CorpusID:259188030}\n}\n```\n\n```bibtex\n@article{Zhu2024HyperConnections,\n title = {Hyper-Connections},\n author = {Defa Zhu and Hongzhi Huang and Zihao Huang and Yutao Zeng and Yunyao Mao and Banggu Wu and Qiyang Min and Xun Zhou},\n journal = {ArXiv},\n year = {2024},\n volume = {abs/2409.19606},\n url = {https://api.semanticscholar.org/CorpusID:272987528}\n}\n```\n\n```bibtex\n@inproceedings{Sun2025F5RTTSIF,\n title = {F5R-TTS: Improving Flow-Matching based Text-to-Speech with Group Relative Policy Optimization},\n author = {Xiaohui Sun and Ruitong Xiao and Jianye Mo and Bowen Wu and Qun Yu and Baoxun Wang},\n year = {2025},\n url = {https://api.semanticscholar.org/CorpusID:277510064}\n}\n```\n\n```bibtex\n@inproceedings{Wang2025EvolutionaryPO,\n title = {Evolutionary Policy Optimization},\n author = {Jianren Wang and Yifan Su and Abhinav Gupta and Deepak Pathak},\n year = {2025},\n url = {https://api.semanticscholar.org/CorpusID:277313729}\n}\n```\n\n```bibtex\n@misc{PI2025,\n title = {Real-Time Action Chunking with Large Models},\n author = {Kevin Black, Manuel Y. Galliker, Sergey Levine},\n year = {2025},\n url = {https://www.pi.website/research/real_time_chunking}\n}\n```\n\n```bibtex\n@misc{PI2025,\n title = {VLAs that Train Fast, Run Fast, and Generalize Better},\n author = {Danny Driess, Jost Tobias Springenberg, Brian Ichter, Lili Yu, Adrian Li-Bell, Karl Pertsch, Allen Z. Ren, Homer Walke, Quan Vuong, Lucy Xiaoyang Shi, Sergey Levine},\n year = {2025},\n url = {https://www.physicalintelligence.company/research/knowledge_insulation}\n}\n```\n\n```bibtex\n@inproceedings{Wagenmaker2025SteeringYD,\n title = {Steering Your Diffusion Policy with Latent Space Reinforcement Learning},\n author = {Andrew Wagenmaker and Mitsuhiko Nakamoto and Yunchu Zhang and Seohong Park and Waleed Yagoub and Anusha Nagabandi and Abhishek Gupta and Sergey Levine},\n year = {2025},\n url = {https://api.semanticscholar.org/CorpusID:279464702}\n}\n```\n\n```bibtex\n@misc{dong2025reinforcementlearningimplicitimitation,\n title = {Reinforcement Learning via Implicit Imitation Guidance}, \n author = {Perry Dong and Alec M. Lessing and Annie S. Chen and Chelsea Finn},\n year = {2025},\n eprint = {2506.07505},\n archivePrefix = {arXiv},\n primaryClass = {cs.LG},\n url = {https://arxiv.org/abs/2506.07505}, \n}\n```\n\n```bibtex\n@misc{zhou2025efficientonlinereinforcementlearning,\n title = {Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data}, \n author = {Zhiyuan Zhou and Andy Peng and Qiyang Li and Sergey Levine and Aviral Kumar},\n year = {2025},\n eprint = {2412.07762},\n archivePrefix = {arXiv},\n primaryClass = {cs.LG},\n url = {https://arxiv.org/abs/2412.07762}, \n}\n```\n\n```bibtex\n@misc{cheang2025gr3technicalreport,\n title = {GR-3 Technical Report}, \n author = {Chilam Cheang and Sijin Chen and Zhongren Cui and Yingdong Hu and Liqun Huang and Tao Kong and Hang Li and Yifeng Li and Yuxiao Liu and Xiao Ma and Hao Niu and Wenxuan Ou and Wanli Peng and Zeyu Ren and Haixin Shi and Jiawen Tian and Hongtao Wu and Xin Xiao and Yuyang Xiao and Jiafeng Xu and Yichu Yang},\n year = {2025},\n eprint = {2507.15493},\n archivePrefix = {arXiv},\n primaryClass = {cs.RO},\n url = {https://arxiv.org/abs/2507.15493}, \n}\n```\n\n```bibtex\n@misc{heng2025vitacformerlearningcrossmodalrepresentation,\n title = {ViTacFormer: Learning Cross-Modal Representation for Visuo-Tactile Dexterous Manipulation}, \n author = {Liang Heng and Haoran Geng and Kaifeng Zhang and Pieter Abbeel and Jitendra Malik},\n year = {2025},\n eprint = {2506.15953},\n archivePrefix = {arXiv},\n primaryClass = {cs.RO},\n url = {https://arxiv.org/abs/2506.15953}, \n}\n```\n\n[*dear alice*](https://www.youtube.com/watch?v=z-Ng5ZvrDm4)\n",
"bugtrack_url": null,
"license": "MIT License\n \n Copyright (c) 2024 Phil Wang\n \n Permission is hereby granted, free of charge, to any person obtaining a copy\n of this software and associated documentation files (the \"Software\"), to deal\n in the Software without restriction, including without limitation the rights\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n copies of the Software, and to permit persons to whom the Software is\n furnished to do so, subject to the following conditions:\n \n The above copyright notice and this permission notice shall be included in all\n copies or substantial portions of the Software.\n \n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n SOFTWARE.",
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