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

docs/source/async.mdx

@@ -196,7 +196,7 @@ client_cfg = RobotClientConfig(
     server_address="localhost:8080",
     policy_device="mps",
     policy_type="smolvla",
-    pretrained_name_or_path="fracapuano/smolvla_async",
+    pretrained_name_or_path="<user>/smolvla_async",
     chunk_size_threshold=0.5,
     actions_per_chunk=50,  # make sure this is less than the max actions of the policy
 )

examples/dataset/load_lerobot_dataset.py

@@ -34,6 +34,8 @@ from huggingface_hub import HfApi
 import lerobot
 from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
 
+
+def main():
     # We ported a number of existing datasets ourselves, use this to see the list:
     print("List of available datasets:")
     pprint(lerobot.available_datasets)
@@ -132,7 +134,6 @@ print(f"\n{dataset[0][camera_key].shape=}")  # (4, c, h, w)
     print(f"{dataset[0]['observation.state'].shape=}")  # (6, c)
     print(f"{dataset[0]['action'].shape=}\n")  # (64, c)
 
-if __name__ == "__main__":
     dataloader = torch.utils.data.DataLoader(
         dataset,
         num_workers=4,
@@ -144,3 +145,7 @@ if __name__ == "__main__":
         print(f"{batch['observation.state'].shape=}")  # (32, 6, c)
         print(f"{batch['action'].shape=}")  # (32, 64, c)
         break
+
+
+if __name__ == "__main__":
+    main()

examples/lekiwi/evaluate.py

@@ -33,6 +33,8 @@ TASK_DESCRIPTION = "My task description"
 HF_MODEL_ID = "<hf_username>/<model_repo_id>"
 HF_DATASET_ID = "<hf_username>/<eval_dataset_repo_id>"
 
+
+def main():
     # Create the robot configuration & robot
     robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
 
@@ -136,3 +138,7 @@ listener.stop()
 
     dataset.finalize()
     dataset.push_to_hub()
+
+
+if __name__ == "__main__":
+    main()

examples/lekiwi/record.py

@@ -34,6 +34,8 @@ RESET_TIME_SEC = 10
 TASK_DESCRIPTION = "My task description"
 HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
 
+
+def main():
     # Create the robot and teleoperator configurations
     robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
     leader_arm_config = SO100LeaderConfig(port="/dev/tty.usbmodem585A0077581", id="my_awesome_leader_arm")
@@ -133,3 +135,7 @@ listener.stop()
 
     dataset.finalize()
     dataset.push_to_hub()
+
+
+if __name__ == "__main__":
+    main()

examples/lekiwi/replay.py

@@ -25,6 +25,8 @@ from lerobot.utils.utils import log_say
 
 EPISODE_IDX = 0
 
+
+def main():
     # Initialize the robot config
     robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
 
@@ -59,3 +61,7 @@ for idx in range(len(episode_frames)):
         busy_wait(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
 
     robot.disconnect()
+
+
+if __name__ == "__main__":
+    main()

examples/lekiwi/teleoperate.py

@@ -24,6 +24,8 @@ from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
 
 FPS = 30
 
+
+def main():
     # Create the robot and teleoperator configurations
     robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="my_lekiwi")
     teleop_arm_config = SO100LeaderConfig(port="/dev/tty.usbmodem585A0077581", id="my_awesome_leader_arm")
@@ -70,3 +72,7 @@ while True:
         log_rerun_data(observation=observation, action=action)
 
         busy_wait(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))
+
+
+if __name__ == "__main__":
+    main()

examples/phone_to_so100/evaluate.py

@@ -52,6 +52,8 @@ TASK_DESCRIPTION = "My task description"
 HF_MODEL_ID = "<hf_username>/<model_repo_id>"
 HF_DATASET_ID = "<hf_username>/<dataset_repo_id>"
 
+
+def main():
     # Create the robot configuration & robot
     camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
     robot_config = SO100FollowerConfig(
@@ -89,7 +91,9 @@ robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotOb
     # Build pipeline to convert joints observation to EE observation
     robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
         steps=[
-        ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys()))
+            ForwardKinematicsJointsToEE(
+                kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys())
+            )
         ],
         to_transition=observation_to_transition,
         to_output=transition_to_observation,
@@ -197,3 +201,7 @@ listener.stop()
 
     dataset.finalize()
     dataset.push_to_hub()
+
+
+if __name__ == "__main__":
+    main()

examples/phone_to_so100/record.py

@@ -50,6 +50,8 @@ RESET_TIME_SEC = 30
 TASK_DESCRIPTION = "My task description"
 HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
 
+
+def main():
     # Create the robot and teleoperator configurations
     camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
     robot_config = SO100FollowerConfig(
@@ -72,7 +74,9 @@ kinematics_solver = RobotKinematics(
     )
 
     # Build pipeline to convert phone action to EE action
-phone_to_robot_ee_pose_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+    phone_to_robot_ee_pose_processor = RobotProcessorPipeline[
+        tuple[RobotAction, RobotObservation], RobotAction
+    ](
         steps=[
             MapPhoneActionToRobotAction(platform=teleop_config.phone_os),
             EEReferenceAndDelta(
@@ -107,7 +111,9 @@ robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotOb
     # Build pipeline to convert joint observation to EE observation
     robot_joints_to_ee_pose = RobotProcessorPipeline[RobotObservation, RobotObservation](
         steps=[
-        ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys()))
+            ForwardKinematicsJointsToEE(
+                kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys())
+            )
         ],
         to_transition=observation_to_transition,
         to_output=transition_to_observation,
@@ -147,7 +153,6 @@ init_rerun(session_name="phone_so100_record")
     if not robot.is_connected or not phone.is_connected:
         raise ValueError("Robot or teleop is not connected!")
 
-
     print("Starting record loop. Move your phone to teleoperate the robot...")
     episode_idx = 0
     while episode_idx < NUM_EPISODES and not events["stop_recording"]:
@@ -203,3 +208,7 @@ listener.stop()
 
     dataset.finalize()
     dataset.push_to_hub()
+
+
+if __name__ == "__main__":
+    main()

examples/phone_to_so100/replay.py

@@ -35,6 +35,8 @@ from lerobot.utils.utils import log_say
 EPISODE_IDX = 0
 HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
 
+
+def main():
     # Initialize the robot config
     robot_config = SO100FollowerConfig(
         port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", use_degrees=True
@@ -98,3 +100,7 @@ for idx in range(len(episode_frames)):
 
     # Clean up
     robot.disconnect()
+
+
+if __name__ == "__main__":
+    main()

examples/phone_to_so100/teleoperate.py

@@ -37,6 +37,8 @@ from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
 
 FPS = 30
 
+
+def main():
     # Initialize the robot and teleoperator
     robot_config = SO100FollowerConfig(
         port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", use_degrees=True
@@ -55,7 +57,9 @@ kinematics_solver = RobotKinematics(
     )
 
     # Build pipeline to convert phone action to ee pose action to joint action
-phone_to_robot_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+    phone_to_robot_joints_processor = RobotProcessorPipeline[
+        tuple[RobotAction, RobotObservation], RobotAction
+    ](
         steps=[
             MapPhoneActionToRobotAction(platform=teleop_config.phone_os),
             EEReferenceAndDelta(
@@ -111,3 +115,7 @@ while True:
         log_rerun_data(observation=phone_obs, action=joint_action)
 
         busy_wait(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))
+
+
+if __name__ == "__main__":
+    main()

examples/so100_to_so100_EE/evaluate.py

@@ -52,6 +52,8 @@ TASK_DESCRIPTION = "My task description"
 HF_MODEL_ID = "<hf_username>/<model_repo_id>"
 HF_DATASET_ID = "<hf_username>/<dataset_repo_id>"
 
+
+def main():
     # Create the robot configuration & robot
     camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
     robot_config = SO100FollowerConfig(
@@ -89,13 +91,14 @@ robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotOb
     # Build pipeline to convert joints observation to EE observation
     robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
         steps=[
-        ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys()))
+            ForwardKinematicsJointsToEE(
+                kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys())
+            )
         ],
         to_transition=observation_to_transition,
         to_output=transition_to_observation,
     )
 
-
     # Create the dataset
     dataset = LeRobotDataset.create(
         repo_id=HF_DATASET_ID,
@@ -198,3 +201,7 @@ listener.stop()
 
     dataset.finalize()
     dataset.push_to_hub()
+
+
+if __name__ == "__main__":
+    main()

examples/so100_to_so100_EE/record.py

@@ -48,10 +48,15 @@ RESET_TIME_SEC = 30
 TASK_DESCRIPTION = "My task description"
 HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
 
+
+def main():
     # Create the robot and teleoperator configurations
     camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
     follower_config = SO100FollowerConfig(
-    port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", cameras=camera_config, use_degrees=True
+        port="/dev/tty.usbmodem5A460814411",
+        id="my_awesome_follower_arm",
+        cameras=camera_config,
+        use_degrees=True,
     )
     leader_config = SO100LeaderConfig(port="/dev/tty.usbmodem5A460819811", id="my_awesome_leader_arm")
 
@@ -135,7 +140,6 @@ dataset = LeRobotDataset.create(
         image_writer_threads=4,
     )
 
-
     # Connect the robot and teleoperator
     leader.connect()
     follower.connect()
@@ -202,3 +206,7 @@ listener.stop()
 
     dataset.finalize()
     dataset.push_to_hub()
+
+
+if __name__ == "__main__":
+    main()

examples/so100_to_so100_EE/replay.py

@@ -36,6 +36,8 @@ from lerobot.utils.utils import log_say
 EPISODE_IDX = 0
 HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
 
+
+def main():
     # Initialize the robot config
     robot_config = SO100FollowerConfig(
         port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", use_degrees=True
@@ -99,3 +101,7 @@ for idx in range(len(episode_frames)):
 
     # Clean up
     robot.disconnect()
+
+
+if __name__ == "__main__":
+    main()

examples/so100_to_so100_EE/teleoperate.py

@@ -37,6 +37,8 @@ from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
 
 FPS = 30
 
+
+def main():
     # Initialize the robot and teleoperator config
     follower_config = SO100FollowerConfig(
         port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", use_degrees=True
@@ -119,3 +121,7 @@ while True:
         log_rerun_data(observation=leader_ee_act, action=follower_joints_act)
 
         busy_wait(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))
+
+
+if __name__ == "__main__":
+    main()

examples/tutorial/act/act_training_example.py

@@ -19,6 +19,7 @@ def make_delta_timestamps(delta_indices: list[int] | None, fps: int) -> list[flo
     return [i / fps for i in delta_indices]
 
 
+def main():
     output_directory = Path("outputs/robot_learning_tutorial/act")
     output_directory.mkdir(parents=True, exist_ok=True)
 
@@ -48,7 +49,8 @@ delta_timestamps = {
 
     # add image features if they are present
     delta_timestamps |= {
-    k: make_delta_timestamps(cfg.observation_delta_indices, dataset_metadata.fps) for k in cfg.image_features
+        k: make_delta_timestamps(cfg.observation_delta_indices, dataset_metadata.fps)
+        for k in cfg.image_features
     }
 
     # Instantiate the dataset
@@ -93,6 +95,10 @@ preprocessor.save_pretrained(output_directory)
     postprocessor.save_pretrained(output_directory)
 
     # Save all assets to the Hub
-policy.push_to_hub("fracapuano/robot_learning_tutorial_act")
-preprocessor.push_to_hub("fracapuano/robot_learning_tutorial_act")
-postprocessor.push_to_hub("fracapuano/robot_learning_tutorial_act")
+    policy.push_to_hub("<user>/robot_learning_tutorial_act")
+    preprocessor.push_to_hub("<user>/robot_learning_tutorial_act")
+    postprocessor.push_to_hub("<user>/robot_learning_tutorial_act")
+
+
+if __name__ == "__main__":
+    main()

examples/tutorial/act/act_using_example.py

@@ -8,8 +8,13 @@ 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
 
+MAX_EPISODES = 5
+MAX_STEPS_PER_EPISODE = 20
+
+
+def main():
     device = torch.device("mps")  # or "cuda" or "cpu"
-model_id = "fracapuano/robot_learning_tutorial_act"
+    model_id = "<user>/robot_learning_tutorial_act"
     model = ACTPolicy.from_pretrained(model_id)
 
     dataset_id = "lerobot/svla_so101_pickplace"
@@ -23,9 +28,6 @@ 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
@@ -55,3 +57,7 @@ for _ in range(MAX_EPISODES):
             robot.send_action(action)
 
         print("Episode finished! Starting new episode...")
+
+
+if __name__ == "__main__":
+    main()

examples/tutorial/async-inf/policy_server.py

@@ -1,6 +1,8 @@
 from lerobot.async_inference.configs import PolicyServerConfig
 from lerobot.async_inference.policy_server import serve
 
+
+def main():
     host = ...  # something like "127.0.0.1" if you're exposing to localhost
     port = ...  # something like 8080
 
@@ -9,3 +11,7 @@ config = PolicyServerConfig(
         port=port,
     )
     serve(config)
+
+
+if __name__ == "__main__":
+    main()

examples/tutorial/async-inf/robot_client.py

@@ -6,6 +6,8 @@ from lerobot.async_inference.robot_client import RobotClient
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
 from lerobot.robots.so100_follower import SO100FollowerConfig
 
+
+def main():
     # these cameras must match the ones expected by the policy - find your cameras with lerobot-find-cameras
     # check the config.json on the Hub for the policy you are using to see the expected camera specs
     camera_cfg = {
@@ -29,7 +31,7 @@ client_cfg = RobotClientConfig(
         server_address=server_address,
         policy_device="mps",
         policy_type="act",
-    pretrained_name_or_path="fracapuano/robot_learning_tutorial_act",
+        pretrained_name_or_path="<user>/robot_learning_tutorial_act",
         chunk_size_threshold=0.5,  # g
         actions_per_chunk=50,  # make sure this is less than the max actions of the policy
     )
@@ -53,3 +55,7 @@ if client.start():
             action_receiver_thread.join()
             # (Optionally) plot the action queue size
             visualize_action_queue_size(client.action_queue_size)
+
+
+if __name__ == "__main__":
+    main()

examples/tutorial/diffusion/diffusion_training_example.py

@@ -19,6 +19,7 @@ def make_delta_timestamps(delta_indices: list[int] | None, fps: int) -> list[flo
     return [i / fps for i in delta_indices]
 
 
+def main():
     output_directory = Path("outputs/robot_learning_tutorial/diffusion")
     output_directory.mkdir(parents=True, exist_ok=True)
 
@@ -49,7 +50,8 @@ delta_timestamps = {
 
     # add image features if they are present
     delta_timestamps |= {
-    k: make_delta_timestamps(cfg.observation_delta_indices, dataset_metadata.fps) for k in cfg.image_features
+        k: make_delta_timestamps(cfg.observation_delta_indices, dataset_metadata.fps)
+        for k in cfg.image_features
     }
 
     # Instantiate the dataset
@@ -94,6 +96,10 @@ preprocessor.save_pretrained(output_directory)
     postprocessor.save_pretrained(output_directory)
 
     # Save all assets to the Hub
-policy.push_to_hub("fracapuano/robot_learning_tutorial_diffusion")
-preprocessor.push_to_hub("fracapuano/robot_learning_tutorial_diffusion")
-postprocessor.push_to_hub("fracapuano/robot_learning_tutorial_diffusion")
+    policy.push_to_hub("<user>/robot_learning_tutorial_diffusion")
+    preprocessor.push_to_hub("<user>/robot_learning_tutorial_diffusion")
+    postprocessor.push_to_hub("<user>/robot_learning_tutorial_diffusion")
+
+
+if __name__ == "__main__":
+    main()

examples/tutorial/diffusion/diffusion_using_example.py

@@ -8,8 +8,13 @@ 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
 
+MAX_EPISODES = 5
+MAX_STEPS_PER_EPISODE = 20
+
+
+def main():
     device = torch.device("mps")  # or "cuda" or "cpu"
-model_id = "fracapuano/robot_learning_tutorial_diffusion"
+    model_id = "<user>/robot_learning_tutorial_diffusion"
 
     model = DiffusionPolicy.from_pretrained(model_id)
 
@@ -20,10 +25,6 @@ preprocess, postprocess = make_pre_post_processors(
         model.config, model_id, dataset_stats=dataset_metadata.stats
     )
 
-MAX_EPISODES = 5
-MAX_STEPS_PER_EPISODE = 20
-
-
     # # find ports using lerobot-find-port
     follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
 
@@ -42,7 +43,6 @@ robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=came
     robot = SO100Follower(robot_cfg)
     robot.connect()
 
-
     for _ in range(MAX_EPISODES):
         for _ in range(MAX_STEPS_PER_EPISODE):
             obs = robot.get_observation()
@@ -58,3 +58,7 @@ for _ in range(MAX_EPISODES):
             robot.send_action(action)
 
         print("Episode finished! Starting new episode...")
+
+
+if __name__ == "__main__":
+    main()

examples/tutorial/pi0/using_pi0_example.py

@@ -11,6 +11,8 @@ from lerobot.robots.so100_follower.so100_follower import SO100Follower
 MAX_EPISODES = 5
 MAX_STEPS_PER_EPISODE = 20
 
+
+def main():
     device = torch.device("mps")  # or "cuda" or "cpu"
     model_id = "lerobot/pi0_base"
 
@@ -65,3 +67,7 @@ for _ in range(MAX_EPISODES):
             robot.send_action(action)
 
         print("Episode finished! Starting new episode...")
+
+
+if __name__ == "__main__":
+    main()

examples/tutorial/rl/hilserl_example.py

@@ -20,6 +20,8 @@ from lerobot.teleoperators.utils import TeleopEvents
 
 LOG_EVERY = 10
 SEND_EVERY = 10
+MAX_EPISODES = 5
+MAX_STEPS_PER_EPISODE = 20
 
 
 def run_learner(
@@ -223,6 +225,7 @@ def make_policy_obs(obs, device: torch.device = "cpu"):
     }
 
 
+def main():
     """Main function - coordinates actor and learner processes."""
 
     device = "mps"  # or "cuda" or "cpu"
@@ -238,15 +241,12 @@ follower_id = ...
     leader_id = ...
 
     # A pretrained model (to be used in-distribution!)
-reward_classifier_id = "fracapuano/reward_classifier_hil_serl_example"
+    reward_classifier_id = "<user>/reward_classifier_hil_serl_example"
     reward_classifier = Classifier.from_pretrained(reward_classifier_id)
 
     reward_classifier.to(device)
     reward_classifier.eval()
 
-MAX_EPISODES = 5
-MAX_STEPS_PER_EPISODE = 20
-
     # Robot and environment configuration
     robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id)
     teleop_cfg = SO100LeaderConfig(port=leader_port, id=leader_id)
@@ -285,13 +285,11 @@ transitions_queue = mp.Queue(maxsize=10)
     parameters_queue = mp.Queue(maxsize=2)
     shutdown_event = mp.Event()
 
-
     # Signal handler for graceful shutdown
     def signal_handler(sig):
         print(f"\nSignal {sig} received, shutting down...")
         shutdown_event.set()
 
-
     signal.signal(signal.SIGINT, signal_handler)
     signal.signal(signal.SIGTERM, signal_handler)
 
@@ -343,3 +341,7 @@ finally:
             learner_process.terminate()
         if actor_process.is_alive():
             actor_process.terminate()
+
+
+if __name__ == "__main__":
+    main()

examples/tutorial/rl/reward_classifier_example.py

@@ -4,6 +4,8 @@ from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.policies.factory import make_policy, make_pre_post_processors
 from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig
 
+
+def main():
     # Device to use for training
     device = "mps"  # or "cuda", or "cpu"
 
@@ -26,8 +28,7 @@ policy = make_policy(config, ds_meta=dataset.meta)
     optimizer = config.get_optimizer_preset().build(policy.parameters())
     preprocessor, _ = make_pre_post_processors(policy_cfg=config, dataset_stats=dataset.meta.stats)
 
-
-classifier_id = "fracapuano/reward_classifier_hil_serl_example"
+    classifier_id = "<user>/reward_classifier_hil_serl_example"
 
     # Instantiate a dataloader
     dataloader = torch.utils.data.DataLoader(dataset, batch_size=16, shuffle=True)
@@ -60,3 +61,7 @@ print("Training finished!")
 
     # You can now save the trained policy.
     policy.push_to_hub(classifier_id)
+
+
+if __name__ == "__main__":
+    main()

examples/tutorial/smolvla/using_smolvla_example.py

@@ -11,6 +11,8 @@ from lerobot.robots.so100_follower.so100_follower import SO100Follower
 MAX_EPISODES = 5
 MAX_STEPS_PER_EPISODE = 20
 
+
+def main():
     device = torch.device("mps")  # or "cuda" or "cpu"
     model_id = "lerobot/smolvla_base"
 
@@ -64,3 +66,7 @@ for _ in range(MAX_EPISODES):
             robot.send_action(action)
 
         print("Episode finished! Starting new episode...")
+
+
+if __name__ == "__main__":
+    main()