mirror of
https://github.com/huggingface/lerobot.git
synced 2026-07-24 18:26:11 +00:00
9d00293f49
Package fitted tokenizer artifacts with processor pipelines so pretrained PI052 checkpoints restore their saved recipe and normalization state without refitting. Co-authored-by: Cursor <cursoragent@cursor.com>
49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
#!/usr/bin/env python
|
|
|
|
# Copyright 2026 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
|
|
|
|
from lerobot.envs.robocasa import RoboCasaEnv, convert_action
|
|
|
|
|
|
def test_robocasa_action_uses_openpi_checkpoint_order():
|
|
action = np.arange(12, dtype=np.float32)
|
|
|
|
converted = convert_action(action)
|
|
|
|
np.testing.assert_array_equal(converted["action.end_effector_position"], [0, 1, 2])
|
|
np.testing.assert_array_equal(converted["action.end_effector_rotation"], [3, 4, 5])
|
|
np.testing.assert_array_equal(converted["action.gripper_close"], [6])
|
|
np.testing.assert_array_equal(converted["action.base_motion"], [7, 8, 9, 10])
|
|
np.testing.assert_array_equal(converted["action.control_mode"], [11])
|
|
|
|
|
|
def test_robocasa_state_uses_openpi_checkpoint_order():
|
|
env = object.__new__(RoboCasaEnv)
|
|
env.obs_type = "pixels_agent_pos"
|
|
env.camera_name = []
|
|
raw_observation = {
|
|
"state.end_effector_position_relative": np.arange(0, 3),
|
|
"state.end_effector_rotation_relative": np.arange(3, 7),
|
|
"state.base_position": np.arange(7, 10),
|
|
"state.base_rotation": np.arange(10, 14),
|
|
"state.gripper_qpos": np.arange(14, 16),
|
|
}
|
|
|
|
observation = env._format_raw_obs(raw_observation)
|
|
|
|
np.testing.assert_array_equal(observation["agent_pos"], np.arange(16, dtype=np.float32))
|