self.root is Path or None + The following packages are already present in the pyproject.toml and will be skipped:

- huggingface-hub

If you want to update it to the latest compatible version, you can use `poetry update package`.
If you prefer to upgrade it to the latest available version, you can use `poetry add package@latest`.

Nothing to add.

lerobot/common/datasets/abstract.py

@@ -32,7 +32,7 @@ class AbstractExperienceReplay(TensorDictReplayBuffer):
     ):
         self.dataset_id = dataset_id
         self.shuffle = shuffle
-        self.root = root
+        self.root = root if root is None else Path(root)
         storage = self._download_or_load_dataset()
 
         super().__init__(
@@ -98,7 +98,7 @@ class AbstractExperienceReplay(TensorDictReplayBuffer):
         if self.root is None:
             self.data_dir = snapshot_download(repo_id=f"cadene/{self.dataset_id}", repo_type="dataset")
         else:
-            self.data_dir = Path(self.root) / self.dataset_id
+            self.data_dir = self.root / self.dataset_id
         return TensorStorage(TensorDict.load_memmap(self.data_dir))
 
     def _compute_stats(self, num_batch=100, batch_size=32):

lerobot/common/datasets/aloha.py

@@ -124,8 +124,9 @@ class AlohaExperienceReplay(AbstractExperienceReplay):
     def image_keys(self) -> list:
         return [("observation", "image", cam) for cam in CAMERAS[self.dataset_id]]
 
-    def _download_and_preproc_obsolete(self, data_dir="data"):
-        raw_dir = Path(data_dir) / f"{self.dataset_id}_raw"
+    def _download_and_preproc_obsolete(self):
+        assert self.root is not None
+        raw_dir = self.root / f"{self.dataset_id}_raw"
         if not raw_dir.is_dir():
             download(raw_dir, self.dataset_id)
 
@@ -174,9 +175,7 @@ class AlohaExperienceReplay(AbstractExperienceReplay):
 
                 if ep_id == 0:
                     # hack to initialize tensordict data structure to store episodes
-                    td_data = (
-                        ep_td[0].expand(total_num_frames).memmap_like(Path(self.root) / f"{self.dataset_id}")
-                    )
+                    td_data = ep_td[0].expand(total_num_frames).memmap_like(self.root / f"{self.dataset_id}")
 
                 td_data[idxtd : idxtd + len(ep_td)] = ep_td
                 idxtd = idxtd + len(ep_td)

lerobot/common/datasets/pusht.py

@@ -112,7 +112,8 @@ class PushtExperienceReplay(AbstractExperienceReplay):
         )
 
     def _download_and_preproc_obsolete(self):
-        raw_dir = Path(self.root) / f"{self.dataset_id}_raw"
+        assert self.root is not None
+        raw_dir = self.root / f"{self.dataset_id}_raw"
         zarr_path = (raw_dir / PUSHT_ZARR).resolve()
         if not zarr_path.is_dir():
             raw_dir.mkdir(parents=True, exist_ok=True)
@@ -208,7 +209,7 @@ class PushtExperienceReplay(AbstractExperienceReplay):
 
             if episode_id == 0:
                 # hack to initialize tensordict data structure to store episodes
-                td_data = ep_td[0].expand(total_frames).memmap_like(Path(self.root) / f"{self.dataset_id}")
+                td_data = ep_td[0].expand(total_frames).memmap_like(self.root / f"{self.dataset_id}")
 
             td_data[idxtd : idxtd + len(ep_td)] = ep_td
 

lerobot/common/datasets/simxarm.py

@@ -65,10 +65,11 @@ class SimxarmExperienceReplay(AbstractExperienceReplay):
         )
 
     def _download_and_preproc_obsolete(self):
+        assert self.root is not None
         # TODO(rcadene): finish download
         download()
 
-        dataset_path = Path(self.root) / "data" / "buffer.pkl"
+        dataset_path = self.root / f"{self.dataset_id}_raw" / "buffer.pkl"
         print(f"Using offline dataset '{dataset_path}'")
         with open(dataset_path, "rb") as f:
             dataset_dict = pickle.load(f)
@@ -110,7 +111,7 @@ class SimxarmExperienceReplay(AbstractExperienceReplay):
 
             if episode_id == 0:
                 # hack to initialize tensordict data structure to store episodes
-                td_data = episode[0].expand(total_frames).memmap_like(Path(self.root) / f"{self.dataset_id}")
+                td_data = episode[0].expand(total_frames).memmap_like(self.root / f"{self.dataset_id}")
 
             td_data[idx0:idx1] = episode