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examples/1_load_lerobot_dataset.py

@@ -77,7 +77,7 @@ print(dataset.hf_dataset)
 
 # LeRobot datasets also subclasses PyTorch datasets so you can do everything you know and love from working
 # with the latter, like iterating through the dataset.
-# The __get_item__ iterates over the frames of the dataset. Since our datasets are also structured by
+# The __getitem__ iterates over the frames of the dataset. Since our datasets are also structured by
 # episodes, you can access the frame indices of any episode using the episode_data_index. Here, we access
 # frame indices associated to the first episode:
 episode_index = 0

examples/6_add_image_transforms.py

@@ -1,7 +1,7 @@
 """
 This script demonstrates how to use torchvision's image transformation with LeRobotDataset for data
 augmentation purposes. The transformations are passed to the dataset as an argument upon creation, and
-transforms are applied to the observation images before they are returned in the dataset's __get_item__.
+transforms are applied to the observation images before they are returned in the dataset's __getitem__.
 """
 
 from pathlib import Path

examples/advanced/2_calculate_validation_loss.py

@@ -8,7 +8,6 @@ especially in the context of imitation learning. The most reliable approach is t
 on the target environment, whether that be in simulation or the real world.
 """
 
-# TODO(aliberts, rcadene): Update this script with the new v2 api
 import math
 from pathlib import Path