pi-zero-pytorch 0.0.34

π0 in Pytorch

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.

Install

$ pip install pi-zero-pytorch

Usage

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)

Contributing

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

Citation

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

dear alice