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#!/usr/bin/env python
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from lerobot.envs.utils import preprocess_observation
from lerobot.processor.env_processor import LiberoProcessorStep
from lerobot.processor.pipeline import PolicyProcessorPipeline
seed = 42
np.random.seed(seed)
B = 5
obs1 = {
"pixels": {
"image": (np.random.rand(B, 256, 256, 3) * 255).astype(np.uint8),
"image2": (np.random.rand(B, 256, 256, 3) * 255).astype(np.uint8),
},
"robot_state": {
"eef": {
"pos": np.random.randn(B, 3),
"quat": np.random.randn(B, 4),
"mat": np.random.randn(B, 3, 3),
},
"gripper": {
"qpos": np.random.randn(B, 2),
"qvel": np.random.randn(B, 2),
},
"joints": {
"pos": np.random.randn(B, 7),
"vel": np.random.randn(B, 7),
},
},
}
observation = preprocess_observation(obs1)
libero_preprocessor = PolicyProcessorPipeline(
steps=[
LiberoProcessorStep(),
]
)
processed_obs = libero_preprocessor(observation)
assert "observation.state" in processed_obs
state = processed_obs["observation.state"]
assert isinstance(state, torch.Tensor)
assert state.dtype == torch.float32
assert state.shape[0] == B
assert state.shape[1] == 8
assert "observation.images.image" in processed_obs
assert "observation.images.image2" in processed_obs
assert isinstance(processed_obs["observation.images.image"], torch.Tensor)
assert isinstance(processed_obs["observation.images.image2"], torch.Tensor)
assert processed_obs["observation.images.image"].shape == (B, 3, 256, 256)
assert processed_obs["observation.images.image2"].shape == (B, 3, 256, 256)
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