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 has been open sourced!
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Einops for the amazing pack and unpack, used extensively here for managing various token sets
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Flex Attention for allowing for easy mixture of autoregressive and bidirectional attention
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@Wonder1905 for the code review and identifying issues
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You? maybe a phd student who wants to contribute to the latest SOTA architecture?
$ pip install pi-zero-pytorchimport 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 EFPO class
from pi_zero_pytorch import π0, EFPO
# 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 EFPO for learning to be orchestrated
epo = EFPO(model)
# gather memories from environment
memories = epo.gather_experience_from_env(mock_env, steps = 10)
# learn from memories
epo.learn_agent(memories, batch_size = 2)At the project root, run
$ pip install '.[test]' # or `uv pip install '.[test]'`Then add your tests to tests/test_pi_zero.py and run
$ pytest tests/That's it
@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},
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}@inproceedings{Sun2025F5RTTSIF,
title = {F5R-TTS: Improving Flow-Matching based Text-to-Speech with Group Relative Policy Optimization},
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}@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},
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}@inproceedings{Wagenmaker2025SteeringYD,
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}@misc{zhou2025efficientonlinereinforcementlearning,
title = {Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data},
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year = {2025},
eprint = {2412.07762},
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}@misc{cheang2025gr3technicalreport,
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}@misc{heng2025vitacformerlearningcrossmodalrepresentation,
title = {ViTacFormer: Learning Cross-Modal Representation for Visuo-Tactile Dexterous Manipulation},
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}@misc{black2025realtimeexecutionactionchunking,
title = {Real-Time Execution of Action Chunking Flow Policies},
author = {Kevin Black and Manuel Y. Galliker and Sergey Levine},
year = {2025},
eprint = {2506.07339},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2506.07339},
}@misc{mcallister2025flowmatchingpolicygradients,
title = {Flow Matching Policy Gradients},
author = {David McAllister and Songwei Ge and Brent Yi and Chung Min Kim and Ethan Weber and Hongsuk Choi and Haiwen Feng and Angjoo Kanazawa},
year = {2025},
eprint = {2507.21053},
archivePrefix = {arXiv},
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}@inproceedings{anonymous2025flow,
title = {Flow Policy Gradients for Legged Robots},
author = {Anonymous},
booktitle = {Submitted to The Fourteenth International Conference on Learning Representations},
year = {2025},
url = {https://openreview.net/forum?id=BA6n0nmagi},
note = {under review}
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year = {2025},
eprint = {2511.14759},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2511.14759},
}@misc{tang2025diffusionmodelsclassifierfreeguidance,
title = {Diffusion Models without Classifier-free Guidance},
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year = {2025},
eprint = {2502.12154},
archivePrefix = {arXiv},
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}@misc{li2025grrlgoingdexterousprecise,
title = {GR-RL: Going Dexterous and Precise for Long-Horizon Robotic Manipulation},
author={Yunfei Li and Xiao Ma and Jiafeng Xu and Yu Cui and Zhongren Cui and Zhigang Han and Liqun Huang and Tao Kong and Yuxiao Liu and Hao Niu and Wanli Peng and Jingchao Qiao and Zeyu Ren and Haixin Shi and Zhi Su and Jiawen Tian and Yuyang Xiao and Shenyu Zhang and Liwei Zheng and Hang Li and Yonghui Wu},
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archivePrefix = {arXiv},
primaryClass = {cs.RO},
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}@misc{black2025trainingtimeactionconditioningefficient,
title = {Training-Time Action Conditioning for Efficient Real-Time Chunking},
author = {Kevin Black and Allen Z. Ren and Michael Equi and Sergey Levine},
year = {2025},
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archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2512.05964},
}