Implementation of Mimic-Video, Video-Action Models for Generalizable Robot Control Beyond VLAs
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Pranoy for submitting a pull request for proprioception masking, fixing the time conditioning of the video model, and Cosmos VAE normalization
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Pranoy for a fix to flow calculation for predict clean objective!
$ pip install mimic-videoimport 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, 5, 3, 32, 32) # 5 frames, 3 channels, 32 x 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)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 testsThat's it
@inproceedings{Pai2025mimicvideoVM,
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year = {2025},
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year = {2025},
eprint = {2511.13720},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
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}@misc{black2025trainingtimeactionconditioningefficient,
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}