pi-zero-pytorch 0.0.1

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).

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)

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}
}