def auto_model_from_pretrained(path, **kwargs):
    import sys
    sys.path.append(path)  # noqa

    map_location = kwargs.pop("map_location", "cpu")
    from modeling_pi0 import PI0Policy
    return PI0Policy.from_pretrained(path, **kwargs).to(map_location)


policy = auto_model_from_pretrained(saved_model_path, map_location="cuda")

state = torch.rand(8)
image = np.array(Image.open("test/example.png"))
image = torch.from_numpy(image / 255).permute(2, 0, 1)

observation = {
    "observation.state": state.unsqueeze(0).to("cuda"),
    "observation.images.image": image.unsqueeze(0).to("cuda"),
    "task": ["put the object in the box"],
}

action_chunk = policy.select_action(observation)[0].cpu().numpy()

HaomingSong/lerobot-pi0-fractal

HaomingSong/
$lerobot-pi0-fractal$
lerobot-pi0-fractal