try fix 9

.github/workflows/release.yml

@@ -83,11 +83,11 @@ jobs:
           fi
 
       - name: Remove Tags with Git dependencies
-        # TODO(Steven): Temporary patch to remove pi from PyPi 0.4.0 release due to its reliance on git dependencies.
+        # TODO(Steven): Temporary patch to remove libero and pi from PyPi 0.4.0 release due to its reliance on git dependencies.
         run: |
           echo "::info:: Checking for Git dependencies to remove from pyproject.toml..."
-          grep -E '@ git\+https|lerobot\[pi\]' pyproject.toml | sed 's/^/::warning:: Removing line: /' || true
+          grep -E '@ git\+https|lerobot\[pi\]|lerobot\[libero\]' pyproject.toml | sed 's/^/::warning:: Removing line: /' || true
-          sed -E -i '/@ git\+https|lerobot\[pi\]/d' pyproject.toml
+          sed -E -i '/@ git\+https|lerobot\[pi\]|lerobot\[libero\]/d' pyproject.toml
           echo "::info:: Git dependencies removed. Proceeding with build."
 
       - name: Install build dependencies

.github/workflows/unbound_deps_tests.yml

@@ -70,7 +70,7 @@ jobs:
           echo "Dependencies unbound:" && cat pyproject.toml
 
       - name: Install lerobot with all extras
-        run: uv sync --all-extras --no-extra groot # TODO(Steven): Make flash-attn optional
+        run: uv sync --all-extras
 
       - name: Run pytest (all extras)
         run: uv run pytest tests -vv

README.md

@@ -186,7 +186,7 @@ For a full list of optional dependencies, see:
 https://pypi.org/project/lerobot/
 
 > [!NOTE]
-> For lerobot 0.4.0, if you want to install pi tags, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
+> For lerobot 0.4.0, if you want to install libero or pi tags, you will have to do: `pip install "lerobot[pi,libero]@git+https://github.com/huggingface/lerobot.git"`.
 >
 > This will be solved in the next patch release
 

docs/source/installation.mdx

@@ -82,7 +82,7 @@ For a full list of optional dependencies, see:
 https://pypi.org/project/lerobot/
 
 > [!NOTE]
-> For lerobot 0.4.0, if you want to install pi, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`
+> For lerobot 0.4.0, if you want to install libero or pi, you will have to do: `pip install "lerobot[pi,libero]@git+https://github.com/huggingface/lerobot.git"`
 
 ### Troubleshooting
 

docs/source/libero.mdx

@@ -28,6 +28,11 @@ LIBERO is now part of our **multi-eval supported simulation**, meaning you can b
 To Install LIBERO, after following LeRobot official instructions, just do:
 `pip install -e ".[libero]"`
 
+> [!NOTE]
+> For lerobot 0.4.0, if you want to install libero tag, you will have to do: `pip install "lerobot[libero]@git+https://github.com/huggingface/lerobot.git"`.
+>
+> This will be solved in the next patch release
+
 ### Single-suite evaluation
 
 Evaluate a policy on one LIBERO suite:

src/lerobot/datasets/lerobot_dataset.py

@@ -19,6 +19,7 @@ import shutil
 import tempfile
 from collections.abc import Callable
 from pathlib import Path
+from typing import Any
 
 import datasets
 import numpy as np
@@ -31,6 +32,8 @@ import torch
 import torch.utils
 from huggingface_hub import HfApi, snapshot_download
 from huggingface_hub.errors import RevisionNotFoundError
+from PIL import Image as PILImage
+from torchvision import transforms
 
 from lerobot.datasets.compute_stats import aggregate_stats, compute_episode_stats
 from lerobot.datasets.image_writer import AsyncImageWriter, write_image
@@ -50,11 +53,9 @@ from lerobot.datasets.utils import (
     get_file_size_in_mb,
     get_hf_features_from_features,
     get_safe_version,
-    hf_transform_to_torch,
     is_valid_version,
     load_episodes,
     load_info,
-    load_nested_dataset,
     load_stats,
     load_tasks,
     update_chunk_file_indices,
@@ -79,6 +80,51 @@ from lerobot.utils.constants import HF_LEROBOT_HOME
 CODEBASE_VERSION = "v3.0"
 
 
+def hf_transform_to_torch(items_dict: dict[str, list[Any]]) -> dict[str, list[torch.Tensor | str]]:
+    """
+    Converts a batch from a Hugging Face dataset to torch tensors.
+    """
+
+    # Create a single ToTensor transform instance to reuse
+    to_tensor = transforms.ToTensor()
+
+    for key in items_dict:
+        items_list = items_dict[key]
+
+        # Check if the list is non-empty
+        if not items_list:
+            continue
+
+        first_item = items_list[0]
+
+        if isinstance(first_item, PILImage.Image):
+            # This is the (slow) CPU-bound part.
+            # We convert every image in the batch list to a tensor.
+            items_dict[key] = [to_tensor(img) for img in items_list]
+
+        elif isinstance(first_item, (str, bytes)):
+            # List of strings (e.g., 'task'), do nothing
+            pass
+
+        elif first_item is None:
+            # List of Nones, do nothing
+            pass
+
+        else:
+            # List of other things (int, float, list, np.ndarray)
+            try:
+                # Convert each item in the list to a tensor
+                items_dict[key] = [torch.tensor(item) for item in items_list]
+            except Exception as e:
+                # This catch is what was missing from the original v3.0 code
+                print(
+                    f"Error converting batch['{key}'] to tensor. First item: {first_item}, Type: {type(first_item)}"
+                )
+                raise e
+
+    return items_dict
+
+
 class LeRobotDatasetMetadata:
     def __init__(
         self,
@@ -693,6 +739,9 @@ class LeRobotDataset(torch.utils.data.Dataset):
             self.repo_id, self.root, self.revision, force_cache_sync=force_cache_sync
         )
 
+        # Pre-load episodes metadata into memory to avoid file I/O in __getitem__
+        self.episodes_metadata_list = [ep for ep in self.meta.episodes]
+
         # Track dataset state for efficient incremental writing
         self._lazy_loading = False
         self._recorded_frames = self.meta.total_frames
@@ -829,8 +878,36 @@ class LeRobotDataset(torch.utils.data.Dataset):
 
     def load_hf_dataset(self) -> datasets.Dataset:
         """hf_dataset contains all the observations, states, actions, rewards, etc."""
+
         features = get_hf_features_from_features(self.features)
-        hf_dataset = load_nested_dataset(self.root / "data", features=features)
+
+        if self.episodes is not None:
+            # Path for episode-specific loading (e.g., visualization)
+            fpaths = set()
+            for ep_idx in self.episodes:
+                ep_meta = self.episodes_metadata_list[ep_idx]
+                chunk_idx = ep_meta["data/chunk_index"]
+                file_idx = ep_meta["data/file_index"]
+                fpath_str = self.meta.data_path.format(chunk_index=chunk_idx, file_index=file_idx)
+                fpaths.add(str(self.root / fpath_str))
+
+            data_files = sorted(list(fpaths))
+
+            hf_dataset = datasets.load_dataset(
+                "parquet", data_files=data_files, features=features, split="train"
+            )
+
+            requested_episodes_set = set(self.episodes)
+            hf_dataset = hf_dataset.filter(
+                lambda x: x["episode_index"] in requested_episodes_set, batched=True, batch_size=1000
+            )
+
+        else:
+            # THIS IS THE FAST PATH FOR TRAINING (self.episodes is None)
+            # Use `data_dir` to trigger the v2.1-style efficient cache.
+            data_dir = str(self.root / "data")
+            hf_dataset = datasets.load_dataset("parquet", data_dir=data_dir, features=features, split="train")
+
         hf_dataset.set_transform(hf_transform_to_torch)
         return hf_dataset
 
@@ -909,7 +986,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             return get_hf_features_from_features(self.features)
 
     def _get_query_indices(self, idx: int, ep_idx: int) -> tuple[dict[str, list[int | bool]]]:
-        ep = self.meta.episodes[ep_idx]
+        ep = self.episodes_metadata_list[ep_idx]
         ep_start = ep["dataset_from_index"]
         ep_end = ep["dataset_to_index"]
         query_indices = {
@@ -940,26 +1017,11 @@ class LeRobotDataset(torch.utils.data.Dataset):
         return query_timestamps
 
     def _query_hf_dataset(self, query_indices: dict[str, list[int]]) -> dict:
-        """
+        return {
-        Query dataset for indices across keys, skipping video keys.
+            key: torch.stack(self.hf_dataset[q_idx][key])
-
+            for key, q_idx in query_indices.items()
-        Tries column-first [key][indices] for speed, falls back to row-first.
+            if key not in self.meta.video_keys
-
+        }
-        Args:
-            query_indices: Dict mapping keys to index lists to retrieve
-
-        Returns:
-            Dict with stacked tensors of queried data (video keys excluded)
-        """
-        result: dict = {}
-        for key, q_idx in query_indices.items():
-            if key in self.meta.video_keys:
-                continue
-            try:
-                result[key] = torch.stack(self.hf_dataset[key][q_idx])
-            except (KeyError, TypeError, IndexError):
-                result[key] = torch.stack(self.hf_dataset[q_idx][key])
-        return result
 
     def _query_videos(self, query_timestamps: dict[str, list[float]], ep_idx: int) -> dict[str, torch.Tensor]:
         """Note: When using data workers (e.g. DataLoader with num_workers>0), do not call this function
@@ -967,7 +1029,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         Segmentation Fault. This probably happens because a memory reference to the video loader is created in
         the main process and a subprocess fails to access it.
         """
-        ep = self.meta.episodes[ep_idx]
+        ep = self.episodes_metadata_list[ep_idx]
         item = {}
         for vid_key, query_ts in query_timestamps.items():
             # Episodes are stored sequentially on a single mp4 to reduce the number of files.
@@ -998,29 +1060,72 @@ class LeRobotDataset(torch.utils.data.Dataset):
     def __getitem__(self, idx) -> dict:
         # Ensure dataset is loaded when we actually need to read from it
         self._ensure_hf_dataset_loaded()
-        item = self.hf_dataset[idx]
-        ep_idx = item["episode_index"].item()
 
+        # 1. Get query indices if deltas are needed
         query_indices = None
+        padding = {}
         if self.delta_indices is not None:
-            query_indices, padding = self._get_query_indices(idx, ep_idx)
+            # We need the episode index *first* to get boundaries.
-            query_result = self._query_hf_dataset(query_indices)
+            # This is a small read for just one item.
-            item = {**item, **padding}
+            ep_idx_only = self.hf_dataset[idx : idx + 1]["episode_index"][0].item()
-            for key, val in query_result.items():
+            query_indices, padding = self._get_query_indices(idx, ep_idx_only)
-                item[key] = val
 
+        # 2. Fetch all data (including images)
+        if query_indices is not None:
+            # --- Delta path ---
+            # Fetch all keys (state, action, AND images) for all deltas
+            item_batch = self.hf_dataset[query_indices["index"]]
+
+            # hf_transform_to_torch (item-level) has already run,
+            # so all values are tensors. We stack them.
+            item = {}
+            for key in item_batch:
+                item[key] = torch.stack(item_batch[key])
+
+            item.update(padding)
+
+            # Use the "current" item's index/timestamp/ep_idx
+            # (assuming 'index' is the key for the main query)
+            current_idx_in_batch = query_indices["index"].index(idx)
+            item["index"] = item["index"][current_idx_in_batch]
+            item["timestamp"] = item["timestamp"][current_idx_in_batch]
+            item["episode_index"] = item["episode_index"][current_idx_in_batch]
+            item["task_index"] = item["task_index"][current_idx_in_batch]
+
+            ep_idx = item["episode_index"].item()
+
+        else:
+            # --- Single-frame path ---
+            item = self.hf_dataset[idx]
+            ep_idx = item["episode_index"].item()
+
+        # 3. Handle videos (which are always separate)
         if len(self.meta.video_keys) > 0:
-            current_ts = item["timestamp"].item()
+            current_ts = (
-            query_timestamps = self._get_query_timestamps(current_ts, query_indices)
+                item["timestamp"].item()
+                if query_indices is None
+                else item["timestamp"][current_idx_in_batch].item()
+            )
+
+            video_query_indices = query_indices
+            if video_query_indices is None:
+                # If no deltas, create a dummy query_indices for _get_query_timestamps
+                video_query_indices = {key: [idx] for key in self.meta.video_keys}
+
+            query_timestamps = self._get_query_timestamps(current_ts, video_query_indices)
             video_frames = self._query_videos(query_timestamps, ep_idx)
+
+            # video_frames are already stacked tensors (B, C, H, W) or (C, H, W)
             item = {**video_frames, **item}
 
+        # 4. Apply image transforms
         if self.image_transforms is not None:
             image_keys = self.meta.camera_keys
             for cam in image_keys:
-                item[cam] = self.image_transforms(item[cam])
+                if cam in item:  # videos or images
+                    item[cam] = self.image_transforms(item[cam])
 
-        # Add task as a string
+        # 5. Add task string
         task_idx = item["task_index"].item()
         item["task"] = self.meta.tasks.iloc[task_idx].name
         return item

src/lerobot/datasets/utils.py

@@ -35,7 +35,6 @@ from datasets.table import embed_table_storage
 from huggingface_hub import DatasetCard, DatasetCardData, HfApi
 from huggingface_hub.errors import RevisionNotFoundError
 from PIL import Image as PILImage
-from torchvision import transforms
 
 from lerobot.configs.types import FeatureType, PolicyFeature
 from lerobot.datasets.backward_compatibility import (
@@ -116,10 +115,15 @@ def load_nested_dataset(pq_dir: Path, features: datasets.Features | None = None)
     if len(paths) == 0:
         raise FileNotFoundError(f"Provided directory does not contain any parquet file: {pq_dir}")
 
+    # Convert Path objects to a list of strings
+    file_paths = [str(path) for path in paths]
+
+    # Use datasets.load_dataset to force creation of an efficient cache
+    # This pre-decodes the images and avoids the on-the-fly bottleneck.
     # TODO(rcadene): set num_proc to accelerate conversion to pyarrow
     with SuppressProgressBars():
-        datasets = Dataset.from_parquet([str(path) for path in paths], features=features)
+        dataset = datasets.load_dataset("parquet", data_files=file_paths, features=features, split="train")
-    return datasets
+    return dataset
 
 
 def get_parquet_num_frames(parquet_path: str | Path) -> int:
@@ -394,33 +398,6 @@ def load_image_as_numpy(
     return img_array
 
 
-def hf_transform_to_torch(items_dict: dict[str, list[Any]]) -> dict[str, list[torch.Tensor | str]]:
-    """Convert a batch from a Hugging Face dataset to torch tensors.
-
-    This transform function converts items from Hugging Face dataset format (pyarrow)
-    to torch tensors. Importantly, images are converted from PIL objects (H, W, C, uint8)
-    to a torch image representation (C, H, W, float32) in the range [0, 1]. Other
-    types are converted to torch.tensor.
-
-    Args:
-        items_dict (dict): A dictionary representing a batch of data from a
-            Hugging Face dataset.
-
-    Returns:
-        dict: The batch with items converted to torch tensors.
-    """
-    for key in items_dict:
-        first_item = items_dict[key][0]
-        if isinstance(first_item, PILImage.Image):
-            to_tensor = transforms.ToTensor()
-            items_dict[key] = [to_tensor(img) for img in items_dict[key]]
-        elif first_item is None:
-            pass
-        else:
-            items_dict[key] = [x if isinstance(x, str) else torch.tensor(x) for x in items_dict[key]]
-    return items_dict
-
-
 def is_valid_version(version: str) -> bool:
     """Check if a string is a valid PEP 440 version.
 

src/lerobot/envs/libero.py

@@ -237,10 +237,9 @@ class LiberoEnv(gym.Env):
     def reset(self, seed=None, **kwargs):
         super().reset(seed=seed)
         self._env.seed(seed)
-        raw_obs = self._env.reset()
         if self.init_states and self._init_states is not None:
             self._env.set_init_state(self._init_states[self._init_state_id])
-            raw_obs = self._env.env._get_observations()
+        raw_obs = self._env.reset()
 
         # After reset, objects may be unstable (slightly floating, intersecting, etc.).
         # Step the simulator with a no-op action for a few frames so everything settles.

tests/envs/test_libero.py

@@ -1,148 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-
-import numpy as np
-import pytest
-
-from lerobot.envs.factory import make_env, make_env_config
-
-# Set MuJoCo rendering backend before importing environment
-os.environ["MUJOCO_GL"] = "egl"
-
-
-def assert_observations_equal(obs1, obs2, path="", atol=1e-8):
-    """
-    Recursively compare two observations and assert they are equal.
-
-    Args:
-        obs1: First observation (dict or numpy array)
-        obs2: Second observation (dict or numpy array)
-        path: Current path in nested structure (for error messages)
-        atol: Absolute tolerance for numpy array comparisons
-    """
-    if isinstance(obs1, dict) and isinstance(obs2, dict):
-        assert obs1.keys() == obs2.keys(), f"Keys differ at {path}: {obs1.keys()} != {obs2.keys()}"
-        for key in obs1:
-            assert_observations_equal(obs1[key], obs2[key], path=f"{path}.{key}" if path else key, atol=atol)
-    elif isinstance(obs1, np.ndarray) and isinstance(obs2, np.ndarray):
-        assert obs1.shape == obs2.shape, f"Shape mismatch at {path}: {obs1.shape} != {obs2.shape}"
-        assert obs1.dtype == obs2.dtype, f"Dtype mismatch at {path}: {obs1.dtype} != {obs2.dtype}"
-        assert np.allclose(obs1, obs2, atol=atol), (
-            f"Array values differ at {path}: max abs diff = {np.abs(obs1 - obs2).max()}"
-        )
-    else:
-        assert type(obs1) is type(obs2), f"Type mismatch at {path}: {type(obs1)} != {type(obs2)}"
-        assert obs1 == obs2, f"Values differ at {path}: {obs1} != {obs2}"
-
-
-def test_libero_env_creation():
-    """Test that the libero environment can be created successfully."""
-    config = make_env_config("libero", task="libero_spatial")
-    envs_dict = make_env(config)
-
-    assert "libero_spatial" in envs_dict
-    assert 0 in envs_dict["libero_spatial"]
-
-    env = envs_dict["libero_spatial"][0]
-    assert env is not None
-
-    # Test basic reset
-    observation, info = env.reset(seed=42)
-    assert observation is not None
-    assert info is not None
-
-    env.close()
-
-
-def test_libero_reset_determinism():
-    """Test that resetting with the same seed produces identical observations."""
-    config = make_env_config("libero", task="libero_spatial")
-    envs_dict = make_env(config)
-    env = envs_dict["libero_spatial"][0]
-
-    # Reset multiple times with the same seed
-    obs1, info1 = env.reset(seed=42)
-    obs2, info2 = env.reset(seed=42)
-    obs3, info3 = env.reset(seed=42)
-
-    # All observations should be identical
-    assert_observations_equal(obs1, obs2)
-    assert_observations_equal(obs1, obs3)
-    assert_observations_equal(obs2, obs3)
-
-    env.close()
-
-
-def test_libero_step_determinism():
-    """Test that step() is deterministic when resetting with the same seed."""
-    config = make_env_config("libero", task="libero_spatial")
-    envs_dict = make_env(config)
-    env = envs_dict["libero_spatial"][0]
-
-    seed = 42
-
-    # First rollout
-    obs1, info1 = env.reset(seed=seed)
-    action = env.action_space.sample()
-    obs_after_step1, reward1, terminated1, truncated1, info_step1 = env.step(action)
-
-    # Second rollout with identical seed and action
-    obs2, info2 = env.reset(seed=seed)
-    obs_after_step2, reward2, terminated2, truncated2, info_step2 = env.step(action)
-
-    # Initial observations should be identical
-    assert_observations_equal(obs1, obs2)
-
-    # Post-step observations should be identical
-    assert_observations_equal(obs_after_step1, obs_after_step2)
-
-    # Rewards and termination flags should be identical
-    assert np.allclose(reward1, reward2), f"Rewards differ: {reward1} != {reward2}"
-    assert np.array_equal(terminated1, terminated2), (
-        f"Terminated flags differ: {terminated1} != {terminated2}"
-    )
-    assert np.array_equal(truncated1, truncated2), f"Truncated flags differ: {truncated1} != {truncated2}"
-
-    env.close()
-
-
-@pytest.mark.parametrize("task", ["libero_spatial", "libero_object", "libero_goal", "libero_10"])
-def test_libero_tasks(task):
-    """Test that different libero tasks can be created and used."""
-    config = make_env_config("libero", task=task)
-    envs_dict = make_env(config)
-
-    assert task in envs_dict
-    assert 0 in envs_dict[task]
-
-    env = envs_dict[task][0]
-    observation, info = env.reset(seed=42)
-
-    assert observation is not None
-    assert info is not None
-
-    # Take a step
-    action = env.action_space.sample()
-    obs, reward, terminated, truncated, info = env.step(action)
-
-    assert obs is not None
-    assert reward is not None
-    assert isinstance(terminated, (bool, np.ndarray))
-    assert isinstance(truncated, (bool, np.ndarray))
-
-    env.close()