Mimic Video
Project description
Mimic Video (wip)
Implementation of Mimic-Video, Video-Action Models for Generalizable Robot Control Beyond VLAs
Appreciation
- Pranoy for submitting a pull request for proprioception masking
Install
$ pip install mimic-video
Usage
import torch
# video wrapper
# but will be agnostic to the model
from mimic_video.cosmos_predict import CosmosPredictWrapper
video_wrapper = CosmosPredictWrapper(
extract_layer = 1,
random_weights = True,
tiny = True
)
# mimic video
from mimic_video import MimicVideo
model = MimicVideo(512, video_wrapper)
# states
video = torch.rand(2, 3, 3, 32, 32)
joint_state = torch.randn(2, 32)
# action
actions = torch.randn(2, 32, 20)
# training
loss = model(
prompts = [
'put the package on the conveyer belt',
'pass the butter'
],
video = video,
actions = actions,
joint_state = joint_state
)
loss.backward()
# inference
actions = model.sample(
prompts = 'peel the orange',
video = video[:1],
joint_state = joint_state[:1]
)
assert actions.shape == (1, 32, 20)
Contributing
First make sure pytest and test dependencies are installed with
$ pip install '.[test]'
Then add your test to tests/test_mimic_video.py and run
$ pytest tests
That's it
Citations
@inproceedings{Pai2025mimicvideoVM,
title = {mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs},
author = {Jonas Pai and Liam Achenbach and Victoriano Montesinos and Benedek Forrai and Oier Mees and Elvis Nava},
year = {2025},
url = {https://api.semanticscholar.org/CorpusID:283920528}
}
@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},
eprint = {2512.05964},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2512.05964},
}
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