Update example 1

examples/1_load_lerobot_dataset.py

@@ -3,10 +3,9 @@ This script demonstrates the use of `LeRobotDataset` class for handling and proc
 It illustrates how to load datasets, manipulate them, and apply transformations suitable for machine learning tasks in PyTorch.
 
 Features included in this script:
-- Loading a dataset and accessing its properties.
+- Viewing a dataset's metadata and exploring its properties.
-- Filtering data by episode number.
+- Loading an existing dataset from the hub or a subset of it.
-- Converting tensor data for visualization.
+- Accessing frames by episode number.
-- Saving video files from dataset frames.
 - Using advanced dataset features like timestamp-based frame selection.
 - Demonstrating compatibility with PyTorch DataLoader for batch processing.
 
@@ -35,7 +34,7 @@ pprint(repo_ids)
 # https://huggingface.co/datasets?other=LeRobot
 
 # Let's take this one for this example
-repo_id = "aliberts/koch_tutorial"
+repo_id = "lerobot/aloha_mobile_cabinet"
 # We can have a look and fetch its metadata to know more about it:
 ds_meta = LeRobotDatasetMetadata(repo_id)
 
@@ -106,15 +105,18 @@ print(dataset.features[camera_key]["shape"])
 # differences with the current loaded frame. For instance:
 delta_timestamps = {
     # loads 4 images: 1 second before current frame, 500 ms before, 200 ms before, and current frame
-    "observation.image": [-1, -0.5, -0.20, 0],
+    camera_key: [-1, -0.5, -0.20, 0],
     # loads 8 state vectors: 1.5 seconds before, 1 second before, ... 200 ms, 100 ms, and current frame
     "observation.state": [-1.5, -1, -0.5, -0.20, -0.10, 0],
     # loads 64 action vectors: current frame, 1 frame in the future, 2 frames, ... 63 frames in the future
     "action": [t / dataset.fps for t in range(64)],
 }
+# Note that in any case, these delta_timestamps values need to be multiples of (1/fps) so that added to any
+# timestamp, you still get a valid timestamp.
+
 dataset = LeRobotDataset(repo_id, delta_timestamps=delta_timestamps)
-print(f"\n{dataset[0]['observation.image'].shape=}")  # (4,c,h,w)
+print(f"\n{dataset[0][camera_key].shape=}")  # (4, c, h, w)
-print(f"{dataset[0]['observation.state'].shape=}")  # (8,c)
+print(f"{dataset[0]['observation.state'].shape=}")  # (6, c)
 print(f"{dataset[0]['action'].shape=}\n")  # (64, c)
 
 # Finally, our datasets are fully compatible with PyTorch dataloaders and samplers because they are just
@@ -127,7 +129,7 @@ dataloader = torch.utils.data.DataLoader(
 )
 
 for batch in dataloader:
-    print(f"{batch['observation.image'].shape=}")  # (32,4,c,h,w)
+    print(f"{batch[camera_key].shape=}")  # (32, 4, c, h, w)
-    print(f"{batch['observation.state'].shape=}")  # (32,8,c)
+    print(f"{batch['observation.state'].shape=}")  # (32, 5, c)
     print(f"{batch['action'].shape=}")  # (32, 64, c)
     break