Fix tolerance for delta_timestamps

lerobot/common/datasets/aloha.py

@@ -67,6 +67,7 @@ def __getitem__(self, idx):
                 item,
                 self.hf_dataset,
                 self.delta_timestamps,
+                tol=1 / self.fps - 1e-4,  # 1e-4 to account for possible numerical error
             )
 
         # convert images from channel last (PIL) to channel first (pytorch)

lerobot/common/datasets/pusht.py

@@ -65,6 +65,7 @@ def __getitem__(self, idx):
                 item,
                 self.hf_dataset,
                 self.delta_timestamps,
+                tol=1 / self.fps - 1e-4,  # 1e-4 to account for possible numerical error
             )
 
         # convert images from channel last (PIL) to channel first (pytorch)

lerobot/common/datasets/utils.py

@@ -11,29 +11,39 @@ def load_previous_and_future_frames(
     item: dict[str, torch.Tensor],
     hf_dataset: datasets.Dataset,
     delta_timestamps: dict[str, list[float]],
-    tol: float = 0.04,
+    tol: float,
 ) -> dict[torch.Tensor]:
     """
-    Given a current item in the dataset containing a timestamp (e.g. 0.6 seconds), and a list of time differences of some modalities (e.g. delta_timestamps={"observation.image": [-0.8, -0.2, 0, 0.2]}),
-    this function computes for each given modality a list of query timestamps (e.g. [-0.2, 0.4, 0.6, 0.8]) and loads the closest frames in the dataset.
-
-    Importantly, when no frame can be found around a query timestamp within a specified tolerance window (e.g. tol=0.04), this function raises an AssertionError.
-    When a timestamp is queried before the first available timestamp of the episode or after the last available timestamp,
-    the violation of the tolerance doesnt raise an AssertionError, and the function populates a boolean array indicating which frames are outside of the episode range.
-    For instance, this boolean array is useful during batched training to not supervise actions associated to timestamps coming after the end of the episode,
-    or to pad the observations in a specific way. Note that by default the observation frames before the start of the episode are the same as the first frame of the episode.
+    Given a current item in the dataset containing a timestamp (e.g. 0.6 seconds), and a list of time differences of
+    some modalities (e.g. delta_timestamps={"observation.image": [-0.8, -0.2, 0, 0.2]}), this function computes for each
+    given modality a list of query timestamps (e.g. [-0.2, 0.4, 0.6, 0.8]) and loads the closest frames in the dataset.
+
+    Importantly, when no frame can be found around a query timestamp within a specified tolerance window, this function
+    raises an AssertionError. When a timestamp is queried before the first available timestamp of the episode or after
+    the last available timestamp, the violation of the tolerance doesnt raise an AssertionError, and the function
+    populates a boolean array indicating which frames are outside of the episode range. For instance, this boolean array
+    is useful during batched training to not supervise actions associated to timestamps coming after the end of the
+    episode, or to pad the observations in a specific way. Note that by default the observation frames before the start
+    of the episode are the same as the first frame of the episode.
 
     Parameters:
-    - item (dict): A dictionary containing all the data related to a frame. It is the result of `dataset[idx]`. Each key corresponds to a different modality (e.g., "timestamp", "observation.image", "action").
-    - hf_dataset (datasets.Dataset): A dictionary containing the full dataset. Each key corresponds to a different modality (e.g., "timestamp", "observation.image", "action").
-    - delta_timestamps (dict): A dictionary containing lists of delta timestamps for each possible modality to be retrieved. These deltas are added to the item timestamp to form the query timestamps.
-    - tol (float, optional): The tolerance level used to determine if a data point is close enough to the query timestamp. Defaults to 0.04.
+    - item (dict): A dictionary containing all the data related to a frame. It is the result of `dataset[idx]`. Each key
+      corresponds to a different modality (e.g., "timestamp", "observation.image", "action").
+    - hf_dataset (datasets.Dataset): A dictionary containing the full dataset. Each key corresponds to a different
+      modality (e.g., "timestamp", "observation.image", "action").
+    - delta_timestamps (dict): A dictionary containing lists of delta timestamps for each possible modality to be
+      retrieved. These deltas are added to the item timestamp to form the query timestamps.
+    - tol (float, optional): The tolerance level used to determine if a data point is close enough to the query
+      timestamp by asserting `tol > difference`. It is suggested to set `tol` to a smaller value than the
+      smallest expected inter-frame period, but large enough to account for jitter.
 
     Returns:
-    - The same item with the queried frames for each modality specified in delta_timestamps, with an additional key for each modality (e.g. "observation.image_is_pad").
+    - The same item with the queried frames for each modality specified in delta_timestamps, with an additional key for
+      each modality (e.g. "observation.image_is_pad").
 
     Raises:
-    - AssertionError: If any of the frames unexpectedly violate the tolerance level. This could indicate synchronization issues with timestamps during data collection.
+    - AssertionError: If any of the frames unexpectedly violate the tolerance level. This could indicate synchronization
+      issues with timestamps during data collection.
     """
     # get indices of the frames associated to the episode, and their timestamps
     ep_data_id_from = item["episode_data_index_from"].item()

lerobot/common/datasets/xarm.py

@@ -59,6 +59,7 @@ def __getitem__(self, idx):
                 item,
                 self.hf_dataset,
                 self.delta_timestamps,
+                tol=1 / self.fps - 1e-4,  # 1e-4 to account for possible numerical error
             )
 
         # convert images from channel last (PIL) to channel first (pytorch)