sensor-diffusion-policy-proximity-goalcond-100epoch

from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
from lerobot.policies.factory import make_pre_post_processors

# Load model
policy = DiffusionPolicy.from_pretrained("calebescobedo/sensor-diffusion-policy-proximity-goalcond-100epoch")

# Load preprocessor and postprocessor from the same repo
preprocessor, postprocessor = make_pre_post_processors(
    policy_cfg=policy.config,
    pretrained_path="calebescobedo/sensor-diffusion-policy-proximity-goalcond-100epoch"
)

# Prepare inputs
batch = {
    'observation.state': state_tensor,  # (batch, 1, 7) - raw joint positions
    'observation.goal': goal_tensor,  # (batch, 1, 3) - raw goal xyz
    'observation.images.table_camera': table_img,  # (batch, 1, 3, 480, 640) - uint8 [0,255] or float [0,1]
    'observation.proximity': proximity_latent,  # (batch, 1, 128) - encoded proximity sensor latent
}

# Inference
policy.eval()
with torch.no_grad():
    batch = preprocessor(batch)  # Normalizes inputs
    actions = policy.select_action(batch)  # Returns normalized actions
    actions = postprocessor(actions)  # Unnormalizes to raw joint positions