fix environment seeding

add fixes for reproducibility

only try to start env if it is closed

revision

fix normalization and data type

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Tests are passing, Eval pretrained model works, Add gif

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update minor

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Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>

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Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>

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Co-authored-by: Alexander Soare <alexander.soare159@gmail.com>

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Add test_examples.py

lerobot/common/datasets/abstract.py

@@ -9,7 +9,7 @@ import tqdm
 from huggingface_hub import snapshot_download
 from tensordict import TensorDict
 from torchrl.data.replay_buffers.replay_buffers import TensorDictReplayBuffer
-from torchrl.data.replay_buffers.samplers import SliceSampler
+from torchrl.data.replay_buffers.samplers import Sampler
 from torchrl.data.replay_buffers.storages import TensorStorage, _collate_id
 from torchrl.data.replay_buffers.writers import ImmutableDatasetWriter, Writer
 from torchrl.envs.transforms.transforms import Compose
@@ -17,22 +17,56 @@ from torchrl.envs.transforms.transforms import Compose
 HF_USER = "lerobot"
 
 
-class AbstractExperienceReplay(TensorDictReplayBuffer):
+class AbstractDataset(TensorDictReplayBuffer):
+    """
+    AbstractDataset represents a dataset in the context of imitation learning or reinforcement learning.
+    This class is designed to be subclassed by concrete implementations that specify particular types of datasets.
+    These implementations can vary based on the source of the data, the environment the data pertains to,
+    or the specific kind of data manipulation applied.
+
+    Note:
+        - `TensorDictReplayBuffer` is the base class from which `AbstractDataset` inherits. It provides the foundational
+           functionality for storing and retrieving `TensorDict`-like data.
+        - `available_datasets` should be overridden by concrete subclasses to list the specific dataset variants supported.
+           It is expected that these variants correspond to a HuggingFace dataset on the hub.
+           For instance, the `AlohaDataset` which inherites from `AbstractDataset` has 4 available dataset variants:
+            - [aloha_sim_transfer_cube_scripted](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_scripted)
+            - [aloha_sim_insertion_scripted](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_scripted)
+            - [aloha_sim_transfer_cube_human](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_human)
+            - [aloha_sim_insertion_human](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human)
+        - When implementing a concrete class (e.g. `AlohaDataset`, `PushtEnv`, `DiffusionPolicy`), you need to:
+            1. set the required class attributes:
+                - for classes inheriting from `AbstractDataset`: `available_datasets`
+                - for classes inheriting from `AbstractEnv`: `name`, `available_tasks`
+                - for classes inheriting from `AbstractPolicy`: `name`
+            2. update variables in `lerobot/__init__.py` (e.g. `available_envs`, `available_datasets_per_envs`, `available_policies`)
+            3. update variables in `tests/test_available.py` by importing your new class
+    """
+
+    available_datasets: list[str] | None = None
+
     def __init__(
         self,
         dataset_id: str,
         version: str | None = None,
-        batch_size: int = None,
+        batch_size: int | None = None,
         *,
         shuffle: bool = True,
         root: Path | None = None,
         pin_memory: bool = False,
         prefetch: int = None,
-        sampler: SliceSampler = None,
-        collate_fn: Callable = None,
-        writer: Writer = None,
+        sampler: Sampler | None = None,
+        collate_fn: Callable | None = None,
+        writer: Writer | None = None,
         transform: "torchrl.envs.Transform" = None,
     ):
+        assert (
+            self.available_datasets is not None
+        ), "Subclasses of `AbstractDataset` should set the `available_datasets` class attribute."
+        assert (
+            dataset_id in self.available_datasets
+        ), f"The provided dataset ({dataset_id}) is not on the list of available datasets {self.available_datasets}."
+
         self.dataset_id = dataset_id
         self.version = version
         self.shuffle = shuffle