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fix(examples): wrap all of them into a main function (#2524)

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This commit is contained in:
Steven Palma
2025-11-26 14:28:04 +01:00
committed by GitHub
parent 87bee86640
commit 17581a9449
24 changed files with 1672 additions and 1532 deletions
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examples/tutorial/act/act_using_example.py
+43 -37
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@@ -8,50 +8,56 @@ from lerobot.policies.utils import build_inference_frame, make_robot_action
from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
from lerobot.robots.so100_follower.so100_follower import SO100Follower
device = torch.device("mps") # or "cuda" or "cpu"
model_id = "fracapuano/robot_learning_tutorial_act"
model = ACTPolicy.from_pretrained(model_id)
dataset_id = "lerobot/svla_so101_pickplace"
# This only downloads the metadata for the dataset, ~10s of MB even for large-scale datasets
dataset_metadata = LeRobotDatasetMetadata(dataset_id)
preprocess, postprocess = make_pre_post_processors(model.config, dataset_stats=dataset_metadata.stats)
# # find ports using lerobot-find-port
follower_port = ... # something like "/dev/tty.usbmodem58760431631"
# # the robot ids are used the load the right calibration files
follower_id = ... # something like "follower_so100"
MAX_EPISODES = 5
MAX_STEPS_PER_EPISODE = 20
# Robot and environment configuration
# Camera keys must match the name and resolutions of the ones used for training!
# You can check the camera keys expected by a model in the info.json card on the model card on the Hub
camera_config = {
"side": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
"up": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
}
robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
robot = SO100Follower(robot_cfg)
robot.connect()
def main():
device = torch.device("mps") # or "cuda" or "cpu"
model_id = "<user>/robot_learning_tutorial_act"
model = ACTPolicy.from_pretrained(model_id)
for _ in range(MAX_EPISODES):
for _ in range(MAX_STEPS_PER_EPISODE):
obs = robot.get_observation()
obs_frame = build_inference_frame(
observation=obs, ds_features=dataset_metadata.features, device=device
)
dataset_id = "lerobot/svla_so101_pickplace"
# This only downloads the metadata for the dataset, ~10s of MB even for large-scale datasets
dataset_metadata = LeRobotDatasetMetadata(dataset_id)
preprocess, postprocess = make_pre_post_processors(model.config, dataset_stats=dataset_metadata.stats)
obs = preprocess(obs_frame)
# # find ports using lerobot-find-port
follower_port = ... # something like "/dev/tty.usbmodem58760431631"
action = model.select_action(obs)
action = postprocess(action)
# # the robot ids are used the load the right calibration files
follower_id = ... # something like "follower_so100"
action = make_robot_action(action, dataset_metadata.features)
# Robot and environment configuration
# Camera keys must match the name and resolutions of the ones used for training!
# You can check the camera keys expected by a model in the info.json card on the model card on the Hub
camera_config = {
"side": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
"up": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
}
robot.send_action(action)
robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
robot = SO100Follower(robot_cfg)
robot.connect()
print("Episode finished! Starting new episode...")
for _ in range(MAX_EPISODES):
for _ in range(MAX_STEPS_PER_EPISODE):
obs = robot.get_observation()
obs_frame = build_inference_frame(
observation=obs, ds_features=dataset_metadata.features, device=device
)
obs = preprocess(obs_frame)
action = model.select_action(obs)
action = postprocess(action)
action = make_robot_action(action, dataset_metadata.features)
robot.send_action(action)
print("Episode finished! Starting new episode...")
if __name__ == "__main__":
main()