WIP faster act

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

.gitignore

@@ -121,7 +121,6 @@ celerybeat.pid
 # Environments
 .env
 .venv
-env/
 venv/
 ENV/
 env.bak/

examples/4_train_policy_with_script.md

@@ -70,7 +70,7 @@ python lerobot/scripts/train.py policy=act env=aloha
 
 There are two things to note here:
 - Config overrides are passed as `param_name=param_value`.
-- Here we have overridden the defaults section. `policy=act` tells Hydra to use `policy/act.yaml`, and `env=aloha` tells Hydra to use `env/pusht.yaml`.
+- Here we have overridden the defaults section. `policy=act` tells Hydra to use `policy/act.yaml`, and `env=aloha` tells Hydra to use `env/aloha.yaml`.
 
 _As an aside: we've set up all of our configurations so that they reproduce state-of-the-art results from papers in the literature._
 

examples/advanced/1_train_act_pusht/act_pusht.yaml

@@ -28,7 +28,7 @@ training:
   online_steps_between_rollouts: 1
 
   delta_timestamps:
-    action: "[i / ${fps} for i in range(${policy.chunk_size})]"
+    action: "[i / ${fps} for i in range(1, ${policy.chunk_size} + 1)]"
 
 eval:
   n_episodes: 50

lerobot/__init__.py

@@ -45,6 +45,9 @@ import itertools
 
 from lerobot.__version__ import __version__  # noqa: F401
 
+# TODO(rcadene): Improve policies and envs. As of now, an item in `available_policies`
+# refers to a yaml file AND a modeling name. Same for `available_envs` which refers to
+# a yaml file AND a environment name. The difference should be more obvious.
 available_tasks_per_env = {
     "aloha": [
         "AlohaInsertion-v0",
@@ -52,6 +55,7 @@ available_tasks_per_env = {
     ],
     "pusht": ["PushT-v0"],
     "xarm": ["XarmLift-v0"],
+    "dora_aloha_real": ["DoraAloha-v0", "DoraKoch-v0", "DoraReachy2-v0"],
 }
 available_envs = list(available_tasks_per_env.keys())
 
@@ -77,6 +81,23 @@ available_datasets_per_env = {
         "lerobot/xarm_push_medium_image",
         "lerobot/xarm_push_medium_replay_image",
     ],
+    "dora_aloha_real": [
+        "lerobot/aloha_static_battery",
+        "lerobot/aloha_static_candy",
+        "lerobot/aloha_static_coffee",
+        "lerobot/aloha_static_coffee_new",
+        "lerobot/aloha_static_cups_open",
+        "lerobot/aloha_static_fork_pick_up",
+        "lerobot/aloha_static_pingpong_test",
+        "lerobot/aloha_static_pro_pencil",
+        "lerobot/aloha_static_screw_driver",
+        "lerobot/aloha_static_tape",
+        "lerobot/aloha_static_thread_velcro",
+        "lerobot/aloha_static_towel",
+        "lerobot/aloha_static_vinh_cup",
+        "lerobot/aloha_static_vinh_cup_left",
+        "lerobot/aloha_static_ziploc_slide",
+    ],
 }
 
 available_real_world_datasets = [
@@ -108,16 +129,19 @@ available_datasets = list(
     itertools.chain(*available_datasets_per_env.values(), available_real_world_datasets)
 )
 
+# lists all available policies from `lerobot/common/policies` by their class attribute: `name`.
 available_policies = [
     "act",
     "diffusion",
     "tdmpc",
 ]
 
+# keys and values refer to yaml files
 available_policies_per_env = {
     "aloha": ["act"],
     "pusht": ["diffusion"],
     "xarm": ["tdmpc"],
+    "dora_aloha_real": ["act_real"],
 }
 
 env_task_pairs = [(env, task) for env, tasks in available_tasks_per_env.items() for task in tasks]

lerobot/common/datasets/compute_stats.py

@@ -16,17 +16,15 @@
 from copy import deepcopy
 from math import ceil
 
-import datasets
 import einops
 import torch
 import tqdm
 from datasets import Image
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.common.datasets.video_utils import VideoFrame
 
 
-def get_stats_einops_patterns(dataset: LeRobotDataset | datasets.Dataset, num_workers=0):
+def get_stats_einops_patterns(dataset, num_workers=0):
     """These einops patterns will be used to aggregate batches and compute statistics.
 
     Note: We assume the images are in channel first format
@@ -66,9 +64,8 @@ def get_stats_einops_patterns(dataset: LeRobotDataset | datasets.Dataset, num_wo
     return stats_patterns
 
 
-def compute_stats(
-    dataset: LeRobotDataset | datasets.Dataset, batch_size=32, num_workers=16, max_num_samples=None
-):
+def compute_stats(dataset, batch_size=32, num_workers=16, max_num_samples=None):
+    """Compute mean/std and min/max statistics of all data keys in a LeRobotDataset."""
     if max_num_samples is None:
         max_num_samples = len(dataset)
 
@@ -159,3 +156,54 @@ def compute_stats(
             "min": min[key],
         }
     return stats
+
+
+def aggregate_stats(ls_datasets) -> dict[str, torch.Tensor]:
+    """Aggregate stats of multiple LeRobot datasets into one set of stats without recomputing from scratch.
+
+    The final stats will have the union of all data keys from each of the datasets.
+
+    The final stats will have the union of all data keys from each of the datasets. For instance:
+    - new_max = max(max_dataset_0, max_dataset_1, ...)
+    - new_min = min(min_dataset_0, min_dataset_1, ...)
+    - new_mean = (mean of all data)
+    - new_std = (std of all data)
+    """
+    data_keys = set()
+    for dataset in ls_datasets:
+        data_keys.update(dataset.stats.keys())
+    stats = {k: {} for k in data_keys}
+    for data_key in data_keys:
+        for stat_key in ["min", "max"]:
+            # compute `max(dataset_0["max"], dataset_1["max"], ...)`
+            stats[data_key][stat_key] = einops.reduce(
+                torch.stack([d.stats[data_key][stat_key] for d in ls_datasets if data_key in d.stats], dim=0),
+                "n ... -> ...",
+                stat_key,
+            )
+        total_samples = sum(d.num_samples for d in ls_datasets if data_key in d.stats)
+        # Compute the "sum" statistic by multiplying each mean by the number of samples in the respective
+        # dataset, then divide by total_samples to get the overall "mean".
+        # NOTE: the brackets around (d.num_samples / total_samples) are needed tor minimize the risk of
+        # numerical overflow!
+        stats[data_key]["mean"] = sum(
+            d.stats[data_key]["mean"] * (d.num_samples / total_samples)
+            for d in ls_datasets
+            if data_key in d.stats
+        )
+        # The derivation for standard deviation is a little more involved but is much in the same spirit as
+        # the computation of the mean.
+        # Given two sets of data where the statistics are known:
+        # σ_combined = sqrt[ (n1 * (σ1^2 + d1^2) + n2 * (σ2^2 + d2^2)) / (n1 + n2) ]
+        # where d1 = μ1 - μ_combined, d2 = μ2 - μ_combined
+        # NOTE: the brackets around (d.num_samples / total_samples) are needed tor minimize the risk of
+        # numerical overflow!
+        stats[data_key]["std"] = torch.sqrt(
+            sum(
+                (d.stats[data_key]["std"] ** 2 + (d.stats[data_key]["mean"] - stats[data_key]["mean"]) ** 2)
+                * (d.num_samples / total_samples)
+                for d in ls_datasets
+                if data_key in d.stats
+            )
+        )
+    return stats

lerobot/common/datasets/factory.py

@@ -16,9 +16,9 @@
 import logging
 
 import torch
-from omegaconf import OmegaConf
+from omegaconf import ListConfig, OmegaConf
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, MultiLeRobotDataset
 
 
 def resolve_delta_timestamps(cfg):
@@ -35,25 +35,54 @@ def resolve_delta_timestamps(cfg):
                 cfg.training.delta_timestamps[key] = eval(delta_timestamps[key])
 
 
-def make_dataset(
-    cfg,
-    split="train",
-):
-    if cfg.env.name not in cfg.dataset_repo_id:
-        logging.warning(
-            f"There might be a mismatch between your training dataset ({cfg.dataset_repo_id=}) and your "
-            f"environment ({cfg.env.name=})."
+def make_dataset(cfg, split: str = "train") -> LeRobotDataset | MultiLeRobotDataset:
+    """
+    Args:
+        cfg: A Hydra config as per the LeRobot config scheme.
+        split: Select the data subset used to create an instance of LeRobotDataset.
+            All datasets hosted on [lerobot](https://huggingface.co/lerobot) contain only one subset: "train".
+            Thus, by default, `split="train"` selects all the available data. `split` aims to work like the
+            slicer in the hugging face datasets:
+            https://huggingface.co/docs/datasets/v2.19.0/loading#slice-splits
+            As of now, it only supports `split="train[:n]"` to load the first n frames of the dataset or
+            `split="train[n:]"` to load the last n frames. For instance `split="train[:1000]"`.
+    Returns:
+        The LeRobotDataset.
+    """
+    if not isinstance(cfg.dataset_repo_id, (str, ListConfig)):
+        raise ValueError(
+            "Expected cfg.dataset_repo_id to be either a single string to load one dataset or a list of "
+            "strings to load multiple datasets."
         )
 
+    # A soft check to warn if the environment matches the dataset. Don't check if we are using a real world env (dora).
+    if cfg.env.name != "dora":
+        if isinstance(cfg.dataset_repo_id, str):
+            dataset_repo_ids = [cfg.dataset_repo_id]  # single dataset
+        else:
+            dataset_repo_ids = cfg.dataset_repo_id  # multiple datasets
+
+        for dataset_repo_id in dataset_repo_ids:
+            if cfg.env.name not in dataset_repo_id:
+                logging.warning(
+                    f"There might be a mismatch between your training dataset ({dataset_repo_id=}) and your "
+                    f"environment ({cfg.env.name=})."
+                )
+
     resolve_delta_timestamps(cfg)
 
     # TODO(rcadene): add data augmentations
 
-    dataset = LeRobotDataset(
-        cfg.dataset_repo_id,
-        split=split,
-        delta_timestamps=cfg.training.get("delta_timestamps"),
-    )
+    if isinstance(cfg.dataset_repo_id, str):
+        dataset = LeRobotDataset(
+            cfg.dataset_repo_id,
+            split=split,
+            delta_timestamps=cfg.training.get("delta_timestamps"),
+        )
+    else:
+        dataset = MultiLeRobotDataset(
+            cfg.dataset_repo_id, split=split, delta_timestamps=cfg.training.get("delta_timestamps")
+        )
 
     if cfg.get("override_dataset_stats"):
         for key, stats_dict in cfg.override_dataset_stats.items():

lerobot/common/datasets/lerobot_dataset.py

@@ -13,12 +13,16 @@
 # 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 logging
 import os
 from pathlib import Path
+from typing import Callable
 
 import datasets
 import torch
+import torch.utils
 
+from lerobot.common.datasets.compute_stats import aggregate_stats
 from lerobot.common.datasets.utils import (
     calculate_episode_data_index,
     load_episode_data_index,
@@ -42,7 +46,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         version: str | None = CODEBASE_VERSION,
         root: Path | None = DATA_DIR,
         split: str = "train",
-        transform: callable = None,
+        transform: Callable | None = None,
         delta_timestamps: dict[list[float]] | None = None,
     ):
         super().__init__()
@@ -171,7 +175,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
     @classmethod
     def from_preloaded(
         cls,
-        repo_id: str,
+        repo_id: str = "from_preloaded",
         version: str | None = CODEBASE_VERSION,
         root: Path | None = None,
         split: str = "train",
@@ -183,7 +187,15 @@ class LeRobotDataset(torch.utils.data.Dataset):
         stats=None,
         info=None,
         videos_dir=None,
-    ):
+    ) -> "LeRobotDataset":
+        """Create a LeRobot Dataset from existing data and attributes instead of loading from the filesystem.
+
+        It is especially useful when converting raw data into LeRobotDataset before saving the dataset
+        on the filesystem or uploading to the hub.
+
+        Note: Meta-data attributes like `repo_id`, `version`, `root`, etc are optional and potentially
+        meaningless depending on the downstream usage of the return dataset.
+        """
         # create an empty object of type LeRobotDataset
         obj = cls.__new__(cls)
         obj.repo_id = repo_id
@@ -195,6 +207,193 @@ class LeRobotDataset(torch.utils.data.Dataset):
         obj.hf_dataset = hf_dataset
         obj.episode_data_index = episode_data_index
         obj.stats = stats
-        obj.info = info
+        obj.info = info if info is not None else {}
         obj.videos_dir = videos_dir
         return obj
+
+
+class MultiLeRobotDataset(torch.utils.data.Dataset):
+    """A dataset consisting of multiple underlying `LeRobotDataset`s.
+
+    The underlying `LeRobotDataset`s are effectively concatenated, and this class adopts much of the API
+    structure of `LeRobotDataset`.
+    """
+
+    def __init__(
+        self,
+        repo_ids: list[str],
+        version: str | None = CODEBASE_VERSION,
+        root: Path | None = DATA_DIR,
+        split: str = "train",
+        transform: Callable | None = None,
+        delta_timestamps: dict[list[float]] | None = None,
+    ):
+        super().__init__()
+        self.repo_ids = repo_ids
+        # Construct the underlying datasets passing everything but `transform` and `delta_timestamps` which
+        # are handled by this class.
+        self._datasets = [
+            LeRobotDataset(
+                repo_id,
+                version=version,
+                root=root,
+                split=split,
+                delta_timestamps=delta_timestamps,
+                transform=transform,
+            )
+            for repo_id in repo_ids
+        ]
+        # Check that some properties are consistent across datasets. Note: We may relax some of these
+        # consistency requirements in future iterations of this class.
+        for repo_id, dataset in zip(self.repo_ids, self._datasets, strict=True):
+            if dataset.info != self._datasets[0].info:
+                raise ValueError(
+                    f"Detected a mismatch in dataset info between {self.repo_ids[0]} and {repo_id}. This is "
+                    "not yet supported."
+                )
+        # Disable any data keys that are not common across all of the datasets. Note: we may relax this
+        # restriction in future iterations of this class. For now, this is necessary at least for being able
+        # to use PyTorch's default DataLoader collate function.
+        self.disabled_data_keys = set()
+        intersection_data_keys = set(self._datasets[0].hf_dataset.features)
+        for dataset in self._datasets:
+            intersection_data_keys.intersection_update(dataset.hf_dataset.features)
+        if len(intersection_data_keys) == 0:
+            raise RuntimeError(
+                "Multiple datasets were provided but they had no keys common to all of them. The "
+                "multi-dataset functionality currently only keeps common keys."
+            )
+        for repo_id, dataset in zip(self.repo_ids, self._datasets, strict=True):
+            extra_keys = set(dataset.hf_dataset.features).difference(intersection_data_keys)
+            logging.warning(
+                f"keys {extra_keys} of {repo_id} were disabled as they are not contained in all the "
+                "other datasets."
+            )
+            self.disabled_data_keys.update(extra_keys)
+
+        self.version = version
+        self.root = root
+        self.split = split
+        self.transform = transform
+        self.delta_timestamps = delta_timestamps
+        self.stats = aggregate_stats(self._datasets)
+
+    @property
+    def repo_id_to_index(self):
+        """Return a mapping from dataset repo_id to a dataset index automatically created by this class.
+
+        This index is incorporated as a data key in the dictionary returned by `__getitem__`.
+        """
+        return {repo_id: i for i, repo_id in enumerate(self.repo_ids)}
+
+    @property
+    def repo_index_to_id(self):
+        """Return the inverse mapping if repo_id_to_index."""
+        return {v: k for k, v in self.repo_id_to_index}
+
+    @property
+    def fps(self) -> int:
+        """Frames per second used during data collection.
+
+        NOTE: Fow now, this relies on a check in __init__ to make sure all sub-datasets have the same info.
+        """
+        return self._datasets[0].info["fps"]
+
+    @property
+    def video(self) -> bool:
+        """Returns True if this dataset loads video frames from mp4 files.
+
+        Returns False if it only loads images from png files.
+
+        NOTE: Fow now, this relies on a check in __init__ to make sure all sub-datasets have the same info.
+        """
+        return self._datasets[0].info.get("video", False)
+
+    @property
+    def features(self) -> datasets.Features:
+        features = {}
+        for dataset in self._datasets:
+            features.update({k: v for k, v in dataset.features.items() if k not in self.disabled_data_keys})
+        return features
+
+    @property
+    def camera_keys(self) -> list[str]:
+        """Keys to access image and video stream from cameras."""
+        keys = []
+        for key, feats in self.features.items():
+            if isinstance(feats, (datasets.Image, VideoFrame)):
+                keys.append(key)
+        return keys
+
+    @property
+    def video_frame_keys(self) -> list[str]:
+        """Keys to access video frames that requires to be decoded into images.
+
+        Note: It is empty if the dataset contains images only,
+        or equal to `self.cameras` if the dataset contains videos only,
+        or can even be a subset of `self.cameras` in a case of a mixed image/video dataset.
+        """
+        video_frame_keys = []
+        for key, feats in self.features.items():
+            if isinstance(feats, VideoFrame):
+                video_frame_keys.append(key)
+        return video_frame_keys
+
+    @property
+    def num_samples(self) -> int:
+        """Number of samples/frames."""
+        return sum(d.num_samples for d in self._datasets)
+
+    @property
+    def num_episodes(self) -> int:
+        """Number of episodes."""
+        return sum(d.num_episodes for d in self._datasets)
+
+    @property
+    def tolerance_s(self) -> float:
+        """Tolerance in seconds used to discard loaded frames when their timestamps
+        are not close enough from the requested frames. It is only used when `delta_timestamps`
+        is provided or when loading video frames from mp4 files.
+        """
+        # 1e-4 to account for possible numerical error
+        return 1 / self.fps - 1e-4
+
+    def __len__(self):
+        return self.num_samples
+
+    def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
+        if idx >= len(self):
+            raise IndexError(f"Index {idx} out of bounds.")
+        # Determine which dataset to get an item from based on the index.
+        start_idx = 0
+        dataset_idx = 0
+        for dataset in self._datasets:
+            if idx >= start_idx + dataset.num_samples:
+                start_idx += dataset.num_samples
+                dataset_idx += 1
+                continue
+            break
+        else:
+            raise AssertionError("We expect the loop to break out as long as the index is within bounds.")
+        item = self._datasets[dataset_idx][idx - start_idx]
+        item["dataset_index"] = torch.tensor(dataset_idx)
+        for data_key in self.disabled_data_keys:
+            if data_key in item:
+                del item[data_key]
+        return item
+
+    def __repr__(self):
+        return (
+            f"{self.__class__.__name__}(\n"
+            f"  Repository IDs: '{self.repo_ids}',\n"
+            f"  Version: '{self.version}',\n"
+            f"  Split: '{self.split}',\n"
+            f"  Number of Samples: {self.num_samples},\n"
+            f"  Number of Episodes: {self.num_episodes},\n"
+            f"  Type: {'video (.mp4)' if self.video else 'image (.png)'},\n"
+            f"  Recorded Frames per Second: {self.fps},\n"
+            f"  Camera Keys: {self.camera_keys},\n"
+            f"  Video Frame Keys: {self.video_frame_keys if self.video else 'N/A'},\n"
+            f"  Transformations: {self.transform},\n"
+            f")"
+        )

lerobot/common/datasets/push_dataset_to_hub/reachy2_hdf5_format.py

@@ -0,0 +1,189 @@
+#!/usr/bin/env python
+
+# Copyright 2024 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.
+"""
+Contains utilities to process raw data format of HDF5 files like in: https://github.com/tonyzhaozh/act
+"""
+
+import gc
+import re
+import shutil
+from pathlib import Path
+
+import h5py
+import torch
+import tqdm
+from datasets import Dataset, Features, Image, Sequence, Value
+
+from lerobot.common.datasets.push_dataset_to_hub.utils import concatenate_episodes
+from lerobot.common.datasets.utils import (
+    hf_transform_to_torch,
+)
+from lerobot.common.datasets.video_utils import VideoFrame
+
+
+def get_cameras(hdf5_data):
+    # ignore depth channel, not currently handled
+    # TODO(rcadene): add depth
+    rgb_cameras = [key for key in hdf5_data["/observations/images_ids"].keys() if "depth" not in key]  # noqa: SIM118
+    return rgb_cameras
+
+
+def check_format(raw_dir) -> bool:
+    hdf5_paths = list(raw_dir.glob("episode_*.hdf5"))
+    assert len(hdf5_paths) != 0
+    for hdf5_path in hdf5_paths:
+        with h5py.File(hdf5_path, "r") as data:
+            assert "/action" in data
+            assert "/observations/qpos" in data
+
+            assert data["/action"].ndim == 2
+            assert data["/observations/qpos"].ndim == 2
+
+            num_frames = data["/action"].shape[0]
+            assert num_frames == data["/observations/qpos"].shape[0]
+
+            for camera in get_cameras(data):
+                assert num_frames == data[f"/observations/images_ids/{camera}"].shape[0]
+                assert (raw_dir / hdf5_path.name.replace(".hdf5", f"_{camera}.mp4")).exists()
+
+                # assert data[f"/observations/images_ids/{camera}"].ndim == 4
+                # b, h, w, c = data[f"/observations/images_ids/{camera}"].shape
+                # assert c < h and c < w, f"Expect (h,w,c) image format but ({h=},{w=},{c=}) provided."
+
+
+def load_from_raw(raw_dir, out_dir, fps, video, debug):
+    hdf5_files = list(raw_dir.glob("*.hdf5"))
+    ep_dicts = []
+    episode_data_index = {"from": [], "to": []}
+
+    id_from = 0
+    for ep_idx, ep_path in tqdm.tqdm(enumerate(hdf5_files), total=len(hdf5_files)):
+        match = re.search(r"_(\d+).hdf5", ep_path.name)
+        if not match:
+            raise ValueError(ep_path.name)
+        raw_ep_idx = int(match.group(1))
+
+        with h5py.File(ep_path, "r") as ep:
+            num_frames = ep["/action"].shape[0]
+
+            # last step of demonstration is considered done
+            done = torch.zeros(num_frames, dtype=torch.bool)
+            done[-1] = True
+
+            state = torch.from_numpy(ep["/observations/qpos"][:])
+            action = torch.from_numpy(ep["/action"][:])
+            if "/observations/qvel" in ep:
+                velocity = torch.from_numpy(ep["/observations/qvel"][:])
+            if "/observations/effort" in ep:
+                effort = torch.from_numpy(ep["/observations/effort"][:])
+
+            ep_dict = {}
+
+            videos_dir = out_dir / "videos"
+            videos_dir.mkdir(parents=True, exist_ok=True)
+
+            for camera in get_cameras(ep):
+                img_key = f"observation.images.{camera}"
+
+                raw_fname = f"episode_{raw_ep_idx}_{camera}.mp4"
+                new_fname = f"{img_key}_episode_{ep_idx:06d}.mp4"
+                shutil.copy(str(raw_dir / raw_fname), str(videos_dir / new_fname))
+
+                # store the reference to the video frame
+                ep_dict[img_key] = [
+                    {"path": f"videos/{new_fname}", "timestamp": i / fps} for i in range(num_frames)
+                ]
+
+            ep_dict["observation.state"] = state
+            if "/observations/velocity" in ep:
+                ep_dict["observation.velocity"] = velocity
+            if "/observations/effort" in ep:
+                ep_dict["observation.effort"] = effort
+            ep_dict["action"] = action
+            ep_dict["episode_index"] = torch.tensor([ep_idx] * num_frames)
+            ep_dict["frame_index"] = torch.arange(0, num_frames, 1)
+            ep_dict["timestamp"] = torch.arange(0, num_frames, 1) / fps
+            ep_dict["next.done"] = done
+            # TODO(rcadene): add reward and success by computing them in sim
+
+            assert isinstance(ep_idx, int)
+            ep_dicts.append(ep_dict)
+
+            episode_data_index["from"].append(id_from)
+            episode_data_index["to"].append(id_from + num_frames)
+
+        id_from += num_frames
+
+        gc.collect()
+
+        # process first episode only
+        if debug:
+            break
+
+    data_dict = concatenate_episodes(ep_dicts)
+    return data_dict, episode_data_index
+
+
+def to_hf_dataset(data_dict, video) -> Dataset:
+    features = {}
+
+    keys = [key for key in data_dict if "observation.images." in key]
+    for key in keys:
+        if video:
+            features[key] = VideoFrame()
+        else:
+            features[key] = Image()
+
+    features["observation.state"] = Sequence(
+        length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
+    )
+    if "observation.velocity" in data_dict:
+        features["observation.velocity"] = Sequence(
+            length=data_dict["observation.velocity"].shape[1], feature=Value(dtype="float32", id=None)
+        )
+    if "observation.effort" in data_dict:
+        features["observation.effort"] = Sequence(
+            length=data_dict["observation.effort"].shape[1], feature=Value(dtype="float32", id=None)
+        )
+    features["action"] = Sequence(
+        length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)
+    )
+    features["episode_index"] = Value(dtype="int64", id=None)
+    features["frame_index"] = Value(dtype="int64", id=None)
+    features["timestamp"] = Value(dtype="float32", id=None)
+    features["next.done"] = Value(dtype="bool", id=None)
+    features["index"] = Value(dtype="int64", id=None)
+
+    hf_dataset = Dataset.from_dict(data_dict, features=Features(features))
+    hf_dataset.set_transform(hf_transform_to_torch)
+    return hf_dataset
+
+
+def from_raw_to_lerobot_format(raw_dir: Path, out_dir: Path, fps=None, video=True, debug=False):
+    # sanity check
+    check_format(raw_dir)
+
+    if fps is None:
+        fps = 30
+
+    data_dir, episode_data_index = load_from_raw(raw_dir, out_dir, fps, video, debug)
+    hf_dataset = to_hf_dataset(data_dir, video)
+
+    info = {
+        "fps": fps,
+        "video": video,
+    }
+    return hf_dataset, episode_data_index, info

lerobot/common/datasets/sampler.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python
+
+# Copyright 2024 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.
+from typing import Iterator, Union
+
+import torch
+
+
+class EpisodeAwareSampler:
+    def __init__(
+        self,
+        episode_data_index: dict,
+        episode_indices_to_use: Union[list, None] = None,
+        drop_n_first_frames: int = 0,
+        drop_n_last_frames: int = 0,
+        shuffle: bool = False,
+    ):
+        """Sampler that optionally incorporates episode boundary information.
+
+        Args:
+            episode_data_index: Dictionary with keys 'from' and 'to' containing the start and end indices of each episode.
+            episode_indices_to_use: List of episode indices to use. If None, all episodes are used.
+                                    Assumes that episodes are indexed from 0 to N-1.
+            drop_n_first_frames: Number of frames to drop from the start of each episode.
+            drop_n_last_frames: Number of frames to drop from the end of each episode.
+            shuffle: Whether to shuffle the indices.
+        """
+        indices = []
+        for episode_idx, (start_index, end_index) in enumerate(
+            zip(episode_data_index["from"], episode_data_index["to"], strict=True)
+        ):
+            if episode_indices_to_use is None or episode_idx in episode_indices_to_use:
+                indices.extend(
+                    range(start_index.item() + drop_n_first_frames, end_index.item() - drop_n_last_frames)
+                )
+
+        self.indices = indices
+        self.shuffle = shuffle
+
+    def __iter__(self) -> Iterator[int]:
+        if self.shuffle:
+            for i in torch.randperm(len(self.indices)):
+                yield self.indices[i]
+        else:
+            for i in self.indices:
+                yield i
+
+    def __len__(self) -> int:
+        return len(self.indices)

lerobot/common/datasets/utils.py

@@ -59,7 +59,7 @@ def unflatten_dict(d, sep="/"):
     return outdict
 
 
-def hf_transform_to_torch(items_dict):
+def hf_transform_to_torch(items_dict: dict[torch.Tensor | None]):
     """Get a transform function that convert items from Hugging Face dataset (pyarrow)
     to torch tensors. Importantly, images are converted from PIL, which corresponds to
     a channel last representation (h w c) of uint8 type, to a torch image representation
@@ -73,6 +73,8 @@ def hf_transform_to_torch(items_dict):
         elif isinstance(first_item, dict) and "path" in first_item and "timestamp" in first_item:
             # video frame will be processed downstream
             pass
+        elif first_item is None:
+            pass
         else:
             items_dict[key] = [torch.tensor(x) for x in items_dict[key]]
     return items_dict
@@ -318,8 +320,7 @@ def calculate_episode_data_index(hf_dataset: datasets.Dataset) -> Dict[str, torc
 
 
 def reset_episode_index(hf_dataset: datasets.Dataset) -> datasets.Dataset:
-    """
-    Reset the `episode_index` of the provided HuggingFace Dataset.
+    """Reset the `episode_index` of the provided HuggingFace Dataset.
 
     `episode_data_index` (and related functionality such as `load_previous_and_future_frames`) requires the
     `episode_index` to be sorted, continuous (1,1,1 and not 1,2,1) and start at 0.
@@ -338,6 +339,7 @@ def reset_episode_index(hf_dataset: datasets.Dataset) -> datasets.Dataset:
         return example
 
     hf_dataset = hf_dataset.map(modify_ep_idx_func)
+
     return hf_dataset
 
 

lerobot/common/logger.py

@@ -233,9 +233,6 @@ class Logger:
         if self._wandb is not None:
             for k, v in d.items():
                 if not isinstance(v, (int, float, str)):
-                    logging.warning(
-                        f'WandB logging of key "{k}" was ignored as its type is not handled by this wrapper.'
-                    )
                     continue
                 self._wandb.log({f"{mode}/{k}": v}, step=step)
 

lerobot/common/policies/act/configuration_act.py

@@ -25,6 +25,13 @@ class ACTConfig:
     The parameters you will most likely need to change are the ones which depend on the environment / sensors.
     Those are: `input_shapes` and 'output_shapes`.
 
+    Notes on the inputs and outputs:
+        - At least one key starting with "observation.image is required as an input.
+        - If there are multiple keys beginning with "observation.images." they are treated as multiple camera
+          views. Right now we only support all images having the same shape.
+        - May optionally work without an "observation.state" key for the proprioceptive robot state.
+        - "action" is required as an output key.
+
     Args:
         n_obs_steps: Number of environment steps worth of observations to pass to the policy (takes the
             current step and additional steps going back).
@@ -33,15 +40,15 @@ class ACTConfig:
             This should be no greater than the chunk size. For example, if the chunk size size 100, you may
             set this to 50. This would mean that the model predicts 100 steps worth of actions, runs 50 in the
             environment, and throws the other 50 out.
-        input_shapes: A dictionary defining the shapes of the input data for the policy.
-            The key represents the input data name, and the value is a list indicating the dimensions
-            of the corresponding data. For example, "observation.images.top" refers to an input from the
-            "top" camera with dimensions [3, 96, 96], indicating it has three color channels and 96x96 resolution.
-            Importantly, shapes doesn't include batch dimension or temporal dimension.
-        output_shapes: A dictionary defining the shapes of the output data for the policy.
-            The key represents the output data name, and the value is a list indicating the dimensions
-            of the corresponding data. For example, "action" refers to an output shape of [14], indicating
-            14-dimensional actions. Importantly, shapes doesn't include batch dimension or temporal dimension.
+        input_shapes: A dictionary defining the shapes of the input data for the policy. The key represents
+            the input data name, and the value is a list indicating the dimensions of the corresponding data.
+            For example, "observation.image" refers to an input from a camera with dimensions [3, 96, 96],
+            indicating it has three color channels and 96x96 resolution. Importantly, `input_shapes` doesn't
+            include batch dimension or temporal dimension.
+        output_shapes: A dictionary defining the shapes of the output data for the policy. The key represents
+            the output data name, and the value is a list indicating the dimensions of the corresponding data.
+            For example, "action" refers to an output shape of [14], indicating 14-dimensional actions.
+            Importantly, `output_shapes` doesn't include batch dimension or temporal dimension.
         input_normalization_modes: A dictionary with key representing the modality (e.g. "observation.state"),
             and the value specifies the normalization mode to apply. The two available modes are "mean_std"
             which subtracts the mean and divides by the standard deviation and "min_max" which rescale in a

lerobot/common/policies/act/modeling_act.py

@@ -139,25 +139,26 @@ class ACTPolicy(nn.Module, PyTorchModelHubMixin):
         batch = self.normalize_targets(batch)
         actions_hat, (mu_hat, log_sigma_x2_hat) = self.model(batch)
 
-        l1_loss = (
-            F.l1_loss(batch["action"], actions_hat, reduction="none") * ~batch["action_is_pad"].unsqueeze(-1)
-        ).mean()
+        bsize = actions_hat.shape[0]
+        l1_loss = F.l1_loss(batch["action"], actions_hat, reduction="none")
+        l1_loss = l1_loss * ~batch["action_is_pad"].unsqueeze(-1)
+        l1_loss = l1_loss.view(bsize, -1).mean(dim=1)
+
+        out_dict = {}
+        out_dict["l1_loss"] = l1_loss
 
-        loss_dict = {"l1_loss": l1_loss.item()}
         if self.config.use_vae:
             # Calculate Dₖₗ(latent_pdf || standard_normal). Note: After computing the KL-divergence for
             # each dimension independently, we sum over the latent dimension to get the total
             # KL-divergence per batch element, then take the mean over the batch.
             # (See App. B of https://arxiv.org/abs/1312.6114 for more details).
-            mean_kld = (
-                (-0.5 * (1 + log_sigma_x2_hat - mu_hat.pow(2) - (log_sigma_x2_hat).exp())).sum(-1).mean()
-            )
-            loss_dict["kld_loss"] = mean_kld.item()
-            loss_dict["loss"] = l1_loss + mean_kld * self.config.kl_weight
+            kld_loss = (-0.5 * (1 + log_sigma_x2_hat - mu_hat.pow(2) - (log_sigma_x2_hat).exp())).sum(-1)
+            out_dict["loss"] = l1_loss + kld_loss * self.config.kl_weight
         else:
-            loss_dict["loss"] = l1_loss
+            out_dict["loss"] = l1_loss
 
-        return loss_dict
+        out_dict["action"] = self.unnormalize_outputs({"action": actions_hat})["action"]
+        return out_dict
 
 
 class ACT(nn.Module):
@@ -198,27 +199,31 @@ class ACT(nn.Module):
     def __init__(self, config: ACTConfig):
         super().__init__()
         self.config = config
-        # BERT style VAE encoder with input [cls, *joint_space_configuration, *action_sequence].
+        # BERT style VAE encoder with input tokens [cls, robot_state, *action_sequence].
         # The cls token forms parameters of the latent's distribution (like this [*means, *log_variances]).
+        self.use_input_state = "observation.state" in config.input_shapes
         if self.config.use_vae:
             self.vae_encoder = ACTEncoder(config)
             self.vae_encoder_cls_embed = nn.Embedding(1, config.dim_model)
             # Projection layer for joint-space configuration to hidden dimension.
-            self.vae_encoder_robot_state_input_proj = nn.Linear(
-                config.input_shapes["observation.state"][0], config.dim_model
-            )
+            if self.use_input_state:
+                self.vae_encoder_robot_state_input_proj = nn.Linear(
+                    config.input_shapes["observation.state"][0], config.dim_model
+                )
             # Projection layer for action (joint-space target) to hidden dimension.
             self.vae_encoder_action_input_proj = nn.Linear(
-                config.input_shapes["observation.state"][0], config.dim_model
+                config.output_shapes["action"][0], config.dim_model
             )
-            self.latent_dim = config.latent_dim
             # Projection layer from the VAE encoder's output to the latent distribution's parameter space.
-            self.vae_encoder_latent_output_proj = nn.Linear(config.dim_model, self.latent_dim * 2)
-            # Fixed sinusoidal positional embedding the whole input to the VAE encoder. Unsqueeze for batch
+            self.vae_encoder_latent_output_proj = nn.Linear(config.dim_model, config.latent_dim * 2)
+            # Fixed sinusoidal positional embedding for the input to the VAE encoder. Unsqueeze for batch
             # dimension.
+            num_input_token_encoder = 1 + config.chunk_size
+            if self.use_input_state:
+                num_input_token_encoder += 1
             self.register_buffer(
                 "vae_encoder_pos_enc",
-                create_sinusoidal_pos_embedding(1 + 1 + config.chunk_size, config.dim_model).unsqueeze(0),
+                create_sinusoidal_pos_embedding(num_input_token_encoder, config.dim_model).unsqueeze(0),
             )
 
         # Backbone for image feature extraction.
@@ -238,15 +243,17 @@ class ACT(nn.Module):
 
         # Transformer encoder input projections. The tokens will be structured like
         # [latent, robot_state, image_feature_map_pixels].
-        self.encoder_robot_state_input_proj = nn.Linear(
-            config.input_shapes["observation.state"][0], config.dim_model
-        )
-        self.encoder_latent_input_proj = nn.Linear(self.latent_dim, config.dim_model)
+        if self.use_input_state:
+            self.encoder_robot_state_input_proj = nn.Linear(
+                config.input_shapes["observation.state"][0], config.dim_model
+            )
+        self.encoder_latent_input_proj = nn.Linear(config.latent_dim, config.dim_model)
         self.encoder_img_feat_input_proj = nn.Conv2d(
             backbone_model.fc.in_features, config.dim_model, kernel_size=1
         )
         # Transformer encoder positional embeddings.
-        self.encoder_robot_and_latent_pos_embed = nn.Embedding(2, config.dim_model)
+        num_input_token_decoder = 2 if self.use_input_state else 1
+        self.encoder_robot_and_latent_pos_embed = nn.Embedding(num_input_token_decoder, config.dim_model)
         self.encoder_cam_feat_pos_embed = ACTSinusoidalPositionEmbedding2d(config.dim_model // 2)
 
         # Transformer decoder.
@@ -258,6 +265,16 @@ class ACT(nn.Module):
 
         self._reset_parameters()
 
+        self.register_buffer(
+            "latent_sample",
+            torch.zeros(1, config.latent_dim, dtype=torch.float32),
+        )
+
+        self.register_buffer(
+            "decoder_in",
+            torch.zeros(config.chunk_size, 1, config.dim_model, dtype=torch.float32),
+        )
+
     def _reset_parameters(self):
         """Xavier-uniform initialization of the transformer parameters as in the original code."""
         for p in chain(self.encoder.parameters(), self.decoder.parameters()):
@@ -285,7 +302,7 @@ class ACT(nn.Module):
                 "action" in batch
             ), "actions must be provided when using the variational objective in training mode."
 
-        batch_size = batch["observation.state"].shape[0]
+        batch_size = batch["observation.images"].shape[0]
 
         # Prepare the latent for input to the transformer encoder.
         if self.config.use_vae and "action" in batch:
@@ -293,11 +310,16 @@ class ACT(nn.Module):
             cls_embed = einops.repeat(
                 self.vae_encoder_cls_embed.weight, "1 d -> b 1 d", b=batch_size
             )  # (B, 1, D)
-            robot_state_embed = self.vae_encoder_robot_state_input_proj(batch["observation.state"]).unsqueeze(
-                1
-            )  # (B, 1, D)
+            if self.use_input_state:
+                robot_state_embed = self.vae_encoder_robot_state_input_proj(batch["observation.state"])
+                robot_state_embed = robot_state_embed.unsqueeze(1)  # (B, 1, D)
             action_embed = self.vae_encoder_action_input_proj(batch["action"])  # (B, S, D)
-            vae_encoder_input = torch.cat([cls_embed, robot_state_embed, action_embed], axis=1)  # (B, S+2, D)
+
+            if self.use_input_state:
+                vae_encoder_input = [cls_embed, robot_state_embed, action_embed]  # (B, S+2, D)
+            else:
+                vae_encoder_input = [cls_embed, action_embed]
+            vae_encoder_input = torch.cat(vae_encoder_input, axis=1)
 
             # Prepare fixed positional embedding.
             # Note: detach() shouldn't be necessary but leaving it the same as the original code just in case.
@@ -308,27 +330,26 @@ class ACT(nn.Module):
                 vae_encoder_input.permute(1, 0, 2), pos_embed=pos_embed.permute(1, 0, 2)
             )[0]  # select the class token, with shape (B, D)
             latent_pdf_params = self.vae_encoder_latent_output_proj(cls_token_out)
-            mu = latent_pdf_params[:, : self.latent_dim]
+            mu = latent_pdf_params[:, : self.config.latent_dim]
             # This is 2log(sigma). Done this way to match the original implementation.
-            log_sigma_x2 = latent_pdf_params[:, self.latent_dim :]
+            log_sigma_x2 = latent_pdf_params[:, self.config.latent_dim :]
 
             # Sample the latent with the reparameterization trick.
             latent_sample = mu + log_sigma_x2.div(2).exp() * torch.randn_like(mu)
         else:
             # When not using the VAE encoder, we set the latent to be all zeros.
             mu = log_sigma_x2 = None
-            latent_sample = torch.zeros([batch_size, self.latent_dim], dtype=torch.float32).to(
-                batch["observation.state"].device
-            )
+            latent_sample = self.latent_sample
 
         # Prepare all other transformer encoder inputs.
         # Camera observation features and positional embeddings.
         all_cam_features = []
         all_cam_pos_embeds = []
         images = batch["observation.images"]
+
         for cam_index in range(images.shape[-4]):
             cam_features = self.backbone(images[:, cam_index])["feature_map"]
-            cam_pos_embed = self.encoder_cam_feat_pos_embed(cam_features).to(dtype=cam_features.dtype)
+            cam_pos_embed = self.encoder_cam_feat_pos_embed(cam_features)
             cam_features = self.encoder_img_feat_input_proj(cam_features)  # (B, C, h, w)
             all_cam_features.append(cam_features)
             all_cam_pos_embeds.append(cam_pos_embed)
@@ -337,13 +358,15 @@ class ACT(nn.Module):
         cam_pos_embed = torch.cat(all_cam_pos_embeds, axis=-1)
 
         # Get positional embeddings for robot state and latent.
-        robot_state_embed = self.encoder_robot_state_input_proj(batch["observation.state"])  # (B, C)
+        if self.use_input_state:
+            robot_state_embed = self.encoder_robot_state_input_proj(batch["observation.state"])  # (B, C)
         latent_embed = self.encoder_latent_input_proj(latent_sample)  # (B, C)
 
         # Stack encoder input and positional embeddings moving to (S, B, C).
+        encoder_in_feats = [latent_embed, robot_state_embed] if self.use_input_state else [latent_embed]
         encoder_in = torch.cat(
             [
-                torch.stack([latent_embed, robot_state_embed], axis=0),
+                torch.stack(encoder_in_feats, axis=0),
                 einops.rearrange(encoder_in, "b c h w -> (h w) b c"),
             ]
         )
@@ -357,11 +380,7 @@ class ACT(nn.Module):
 
         # Forward pass through the transformer modules.
         encoder_out = self.encoder(encoder_in, pos_embed=pos_embed)
-        decoder_in = torch.zeros(
-            (self.config.chunk_size, batch_size, self.config.dim_model),
-            dtype=pos_embed.dtype,
-            device=pos_embed.device,
-        )
+        decoder_in = self.decoder_in
         decoder_out = self.decoder(
             decoder_in,
             encoder_out,
@@ -561,6 +580,10 @@ class ACTSinusoidalPositionEmbedding2d(nn.Module):
         self._eps = 1e-6
         # Inverse "common ratio" for the geometric progression in sinusoid frequencies.
         self._temperature = 10000
+        self.register_buffer(
+            "inverse_frequency", 
+            self._temperature ** (2 * (torch.arange(self.dimension, dtype=torch.float32) // 2) / self.dimension),
+        )
 
     def forward(self, x: Tensor) -> Tensor:
         """
@@ -572,8 +595,8 @@ class ACTSinusoidalPositionEmbedding2d(nn.Module):
         not_mask = torch.ones_like(x[0, :1])  # (1, H, W)
         # Note: These are like range(1, H+1) and range(1, W+1) respectively, but in most implementations
         # they would be range(0, H) and range(0, W). Keeping it at as is to match the original code.
-        y_range = not_mask.cumsum(1, dtype=torch.float32)
-        x_range = not_mask.cumsum(2, dtype=torch.float32)
+        y_range = not_mask.cumsum(1, dtype=x.dtype)
+        x_range = not_mask.cumsum(2, dtype=x.dtype)
 
         # "Normalize" the position index such that it ranges in [0, 2π].
         # Note: Adding epsilon on the denominator should not be needed as all values of y_embed and x_range
@@ -581,9 +604,7 @@ class ACTSinusoidalPositionEmbedding2d(nn.Module):
         y_range = y_range / (y_range[:, -1:, :] + self._eps) * self._two_pi
         x_range = x_range / (x_range[:, :, -1:] + self._eps) * self._two_pi
 
-        inverse_frequency = self._temperature ** (
-            2 * (torch.arange(self.dimension, dtype=torch.float32, device=x.device) // 2) / self.dimension
-        )
+        inverse_frequency = self.inverse_frequency
 
         x_range = x_range.unsqueeze(-1) / inverse_frequency  # (1, H, W, 1)
         y_range = y_range.unsqueeze(-1) / inverse_frequency  # (1, H, W, 1)

lerobot/common/policies/diffusion/configuration_diffusion.py

@@ -26,21 +26,26 @@ class DiffusionConfig:
     The parameters you will most likely need to change are the ones which depend on the environment / sensors.
     Those are: `input_shapes` and `output_shapes`.
 
+    Notes on the inputs and outputs:
+        - "observation.state" is required as an input key.
+        - A key starting with "observation.image is required as an input.
+        - "action" is required as an output key.
+
     Args:
         n_obs_steps: Number of environment steps worth of observations to pass to the policy (takes the
             current step and additional steps going back).
         horizon: Diffusion model action prediction size as detailed in `DiffusionPolicy.select_action`.
         n_action_steps: The number of action steps to run in the environment for one invocation of the policy.
             See `DiffusionPolicy.select_action` for more details.
-        input_shapes: A dictionary defining the shapes of the input data for the policy.
-            The key represents the input data name, and the value is a list indicating the dimensions
-            of the corresponding data. For example, "observation.image" refers to an input from
-            a camera with dimensions [3, 96, 96], indicating it has three color channels and 96x96 resolution.
-            Importantly, shapes doesnt include batch dimension or temporal dimension.
-        output_shapes: A dictionary defining the shapes of the output data for the policy.
-            The key represents the output data name, and the value is a list indicating the dimensions
-            of the corresponding data. For example, "action" refers to an output shape of [14], indicating
-            14-dimensional actions. Importantly, shapes doesnt include batch dimension or temporal dimension.
+        input_shapes: A dictionary defining the shapes of the input data for the policy. The key represents
+            the input data name, and the value is a list indicating the dimensions of the corresponding data.
+            For example, "observation.image" refers to an input from a camera with dimensions [3, 96, 96],
+            indicating it has three color channels and 96x96 resolution. Importantly, `input_shapes` doesn't
+            include batch dimension or temporal dimension.
+        output_shapes: A dictionary defining the shapes of the output data for the policy. The key represents
+            the output data name, and the value is a list indicating the dimensions of the corresponding data.
+            For example, "action" refers to an output shape of [14], indicating 14-dimensional actions.
+            Importantly, `output_shapes` doesn't include batch dimension or temporal dimension.
         input_normalization_modes: A dictionary with key representing the modality (e.g. "observation.state"),
             and the value specifies the normalization mode to apply. The two available modes are "mean_std"
             which subtracts the mean and divides by the standard deviation and "min_max" which rescale in a

lerobot/common/policies/diffusion/modeling_diffusion.py

@@ -239,10 +239,8 @@ class DiffusionModel(nn.Module):
         global_cond = torch.cat([batch["observation.state"], img_features], dim=-1).flatten(start_dim=1)
 
         # run sampling
-        sample = self.conditional_sample(batch_size, global_cond=global_cond)
+        actions = self.conditional_sample(batch_size, global_cond=global_cond)
 
-        # `horizon` steps worth of actions (from the first observation).
-        actions = sample[..., : self.config.output_shapes["action"][0]]
         # Extract `n_action_steps` steps worth of actions (from the current observation).
         start = n_obs_steps - 1
         end = start + self.config.n_action_steps

lerobot/common/policies/tdmpc/configuration_tdmpc.py

@@ -31,6 +31,15 @@ class TDMPCConfig:
         n_action_repeats: The number of times to repeat the action returned by the planning. (hint: Google
             action repeats in Q-learning or ask your favorite chatbot)
         horizon: Horizon for model predictive control.
+        input_shapes: A dictionary defining the shapes of the input data for the policy. The key represents
+            the input data name, and the value is a list indicating the dimensions of the corresponding data.
+            For example, "observation.image" refers to an input from a camera with dimensions [3, 96, 96],
+            indicating it has three color channels and 96x96 resolution. Importantly, `input_shapes` doesn't
+            include batch dimension or temporal dimension.
+        output_shapes: A dictionary defining the shapes of the output data for the policy. The key represents
+            the output data name, and the value is a list indicating the dimensions of the corresponding data.
+            For example, "action" refers to an output shape of [14], indicating 14-dimensional actions.
+            Importantly, `output_shapes` doesn't include batch dimension or temporal dimension.
         input_normalization_modes: A dictionary with key representing the modality (e.g. "observation.state"),
             and the value specifies the normalization mode to apply. The two available modes are "mean_std"
             which subtracts the mean and divides by the standard deviation and "min_max" which rescale in a

lerobot/common/utils/utils.py

@@ -120,13 +120,13 @@ def init_logging():
     logging.getLogger().addHandler(console_handler)
 
 
-def format_big_number(num):
+def format_big_number(num, precision=0):
     suffixes = ["", "K", "M", "B", "T", "Q"]
     divisor = 1000.0
 
     for suffix in suffixes:
         if abs(num) < divisor:
-            return f"{num:.0f}{suffix}"
+            return f"{num:.{precision}f}{suffix}"
         num /= divisor
 
     return num

lerobot/configs/default.yaml

@@ -23,6 +23,10 @@ use_amp: false
 # `seed` is used for training (eg: model initialization, dataset shuffling)
 # AND for the evaluation environments.
 seed: ???
+# You may provide a list of datasets here. `train.py` creates them all and concatenates them. Note: only data
+# keys common between the datasets are kept. Each dataset gets and additional transform that inserts the
+# "dataset_index" into the returned item. The index mapping is made according to the order in which the
+# datsets are provided.
 dataset_repo_id: lerobot/pusht
 
 training:

lerobot/configs/env/dora_aloha_real.yaml

@@ -0,0 +1,13 @@
+# @package _global_
+
+fps: 30
+
+env:
+  name: dora
+  task: DoraAloha-v0
+  state_dim: 14
+  action_dim: 14
+  fps: ${fps}
+  episode_length: 400
+  gym:
+    fps: ${fps}

lerobot/configs/env/dora_reachy2_real.yaml

@@ -0,0 +1,13 @@
+# @package _global_
+
+fps: 30
+
+env:
+  name: dora
+  task: DoraReachy2-v0
+  state_dim: 22
+  action_dim: 22
+  fps: ${fps}
+  episode_length: 400
+  gym:
+    fps: ${fps}

lerobot/configs/policy/act.yaml

@@ -25,7 +25,7 @@ training:
   online_steps_between_rollouts: 1
 
   delta_timestamps:
-    action: "[i / ${fps} for i in range(${policy.chunk_size})]"
+    action: "[i / ${fps} for i in range(1, ${policy.chunk_size} + 1)]"
 
 eval:
   n_episodes: 50

lerobot/configs/policy/act_reachy2_real.yaml

@@ -0,0 +1,97 @@
+# @package _global_
+
+# Use `act_real.yaml` to train on real-world Aloha/Aloha2 datasets.
+# Compared to `act.yaml`, it contains 4 cameras (i.e. cam_right_wrist, cam_left_wrist, images,
+# cam_low) instead of 1 camera (i.e. top). Also, `training.eval_freq` is set to -1. This config is used
+# to evaluate checkpoints at a certain frequency of training steps. When it is set to -1, it deactivates evaluation.
+# This is because real-world evaluation is done through [dora-lerobot](https://github.com/dora-rs/dora-lerobot).
+# Look at its README for more information on how to evaluate a checkpoint in the real-world.
+#
+# Example of usage for training:
+# ```bash
+# python lerobot/scripts/train.py \
+#   policy=act_real \
+#   env=dora_aloha_real
+# ```
+
+seed: 1000
+dataset_repo_id: cadene/reachy2_teleop_remi
+
+override_dataset_stats:
+  observation.images.cam_trunk:
+    # stats from imagenet, since we use a pretrained vision model
+    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
+    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
+
+training:
+  offline_steps: 80000
+  online_steps: 0
+  eval_freq: -1
+  save_freq: 10000
+  log_freq: 100
+  save_checkpoint: true
+
+  batch_size: 8
+  lr: 1e-5
+  lr_backbone: 1e-5
+  weight_decay: 1e-4
+  grad_clip_norm: 10
+  online_steps_between_rollouts: 1
+
+  delta_timestamps:
+    action: "[i / ${fps} for i in range(1, ${policy.chunk_size} + 1)]"
+
+eval:
+  n_episodes: 50
+  batch_size: 50
+
+# See `configuration_act.py` for more details.
+policy:
+  name: act
+
+  # Input / output structure.
+  n_obs_steps: 1
+  chunk_size: 100 # chunk_size
+  n_action_steps: 100
+
+  input_shapes:
+    # TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env?
+    observation.images.cam_trunk: [3, 800, 1280]
+    observation.state: ["${env.state_dim}"]
+  output_shapes:
+    action: ["${env.action_dim}"]
+
+  # Normalization / Unnormalization
+  input_normalization_modes:
+    observation.images.cam_trunk: mean_std
+    observation.state: mean_std
+  output_normalization_modes:
+    action: mean_std
+
+  # Architecture.
+  # Vision backbone.
+  vision_backbone: resnet18
+  pretrained_backbone_weights: ResNet18_Weights.IMAGENET1K_V1
+  replace_final_stride_with_dilation: false
+  # Transformer layers.
+  pre_norm: false
+  dim_model: 512
+  n_heads: 8
+  dim_feedforward: 3200
+  feedforward_activation: relu
+  n_encoder_layers: 4
+  # Note: Although the original ACT implementation has 7 for `n_decoder_layers`, there is a bug in the code
+  # that means only the first layer is used. Here we match the original implementation by setting this to 1.
+  # See this issue https://github.com/tonyzhaozh/act/issues/25#issue-2258740521.
+  n_decoder_layers: 1
+  # VAE.
+  use_vae: true
+  latent_dim: 32
+  n_vae_encoder_layers: 4
+
+  # Inference.
+  temporal_ensemble_momentum: null
+
+  # Training and loss computation.
+  dropout: 0.1
+  kl_weight: 10.0

lerobot/configs/policy/act_real.yaml

@@ -0,0 +1,115 @@
+# @package _global_
+
+# Use `act_real.yaml` to train on real-world Aloha/Aloha2 datasets.
+# Compared to `act.yaml`, it contains 4 cameras (i.e. cam_right_wrist, cam_left_wrist, images,
+# cam_low) instead of 1 camera (i.e. top). Also, `training.eval_freq` is set to -1. This config is used
+# to evaluate checkpoints at a certain frequency of training steps. When it is set to -1, it deactivates evaluation.
+# This is because real-world evaluation is done through [dora-lerobot](https://github.com/dora-rs/dora-lerobot).
+# Look at its README for more information on how to evaluate a checkpoint in the real-world.
+#
+# Example of usage for training:
+# ```bash
+# python lerobot/scripts/train.py \
+#   policy=act_real \
+#   env=dora_aloha_real
+# ```
+
+seed: 1000
+dataset_repo_id: lerobot/aloha_static_vinh_cup
+
+override_dataset_stats:
+  observation.images.cam_right_wrist:
+    # stats from imagenet, since we use a pretrained vision model
+    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
+    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
+  observation.images.cam_left_wrist:
+    # stats from imagenet, since we use a pretrained vision model
+    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
+    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
+  observation.images.cam_high:
+    # stats from imagenet, since we use a pretrained vision model
+    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
+    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
+  observation.images.cam_low:
+    # stats from imagenet, since we use a pretrained vision model
+    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
+    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
+
+training:
+  offline_steps: 80000
+  online_steps: 0
+  eval_freq: -1
+  save_freq: 10000
+  log_freq: 100
+  save_checkpoint: true
+
+  batch_size: 8
+  lr: 1e-5
+  lr_backbone: 1e-5
+  weight_decay: 1e-4
+  grad_clip_norm: 10
+  online_steps_between_rollouts: 1
+
+  delta_timestamps:
+    action: "[i / ${fps} for i in range(1, ${policy.chunk_size} + 1)]"
+
+eval:
+  n_episodes: 50
+  batch_size: 50
+
+# See `configuration_act.py` for more details.
+policy:
+  name: act
+
+  # Input / output structure.
+  n_obs_steps: 1
+  chunk_size: 100 # chunk_size
+  n_action_steps: 100
+
+  input_shapes:
+    # TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env?
+    observation.images.cam_right_wrist: [3, 480, 640]
+    observation.images.cam_left_wrist: [3, 480, 640]
+    observation.images.cam_high: [3, 480, 640]
+    observation.images.cam_low: [3, 480, 640]
+    observation.state: ["${env.state_dim}"]
+  output_shapes:
+    action: ["${env.action_dim}"]
+
+  # Normalization / Unnormalization
+  input_normalization_modes:
+    observation.images.cam_right_wrist: mean_std
+    observation.images.cam_left_wrist: mean_std
+    observation.images.cam_high: mean_std
+    observation.images.cam_low: mean_std
+    observation.state: mean_std
+  output_normalization_modes:
+    action: mean_std
+
+  # Architecture.
+  # Vision backbone.
+  vision_backbone: resnet18
+  pretrained_backbone_weights: ResNet18_Weights.IMAGENET1K_V1
+  replace_final_stride_with_dilation: false
+  # Transformer layers.
+  pre_norm: false
+  dim_model: 512
+  n_heads: 8
+  dim_feedforward: 3200
+  feedforward_activation: relu
+  n_encoder_layers: 4
+  # Note: Although the original ACT implementation has 7 for `n_decoder_layers`, there is a bug in the code
+  # that means only the first layer is used. Here we match the original implementation by setting this to 1.
+  # See this issue https://github.com/tonyzhaozh/act/issues/25#issue-2258740521.
+  n_decoder_layers: 1
+  # VAE.
+  use_vae: true
+  latent_dim: 32
+  n_vae_encoder_layers: 4
+
+  # Inference.
+  temporal_ensemble_momentum: null
+
+  # Training and loss computation.
+  dropout: 0.1
+  kl_weight: 10.0

lerobot/configs/policy/act_real_no_state.yaml

@@ -0,0 +1,111 @@
+# @package _global_
+
+# Use `act_real_no_state.yaml` to train on real-world Aloha/Aloha2 datasets when cameras are moving (e.g. wrist cameras)
+# Compared to `act_real.yaml`, it is camera only and does not use the state as input which is vector of robot joint positions.
+# We validated experimentaly that not using state reaches better success rate. Our hypothesis is that `act_real.yaml` might
+# overfits to the state, because the images are more complex to learn from since they are moving.
+#
+# Example of usage for training:
+# ```bash
+# python lerobot/scripts/train.py \
+#   policy=act_real_no_state \
+#   env=dora_aloha_real
+# ```
+
+seed: 1000
+dataset_repo_id: lerobot/aloha_static_vinh_cup
+
+override_dataset_stats:
+  observation.images.cam_right_wrist:
+    # stats from imagenet, since we use a pretrained vision model
+    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
+    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
+  observation.images.cam_left_wrist:
+    # stats from imagenet, since we use a pretrained vision model
+    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
+    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
+  observation.images.cam_high:
+    # stats from imagenet, since we use a pretrained vision model
+    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
+    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
+  observation.images.cam_low:
+    # stats from imagenet, since we use a pretrained vision model
+    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
+    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
+
+training:
+  offline_steps: 80000
+  online_steps: 0
+  eval_freq: -1
+  save_freq: 10000
+  log_freq: 100
+  save_checkpoint: true
+
+  batch_size: 8
+  lr: 1e-5
+  lr_backbone: 1e-5
+  weight_decay: 1e-4
+  grad_clip_norm: 10
+  online_steps_between_rollouts: 1
+
+  delta_timestamps:
+    action: "[i / ${fps} for i in range(1, ${policy.chunk_size} + 1)]"
+
+eval:
+  n_episodes: 50
+  batch_size: 50
+
+# See `configuration_act.py` for more details.
+policy:
+  name: act
+
+  # Input / output structure.
+  n_obs_steps: 1
+  chunk_size: 100 # chunk_size
+  n_action_steps: 100
+
+  input_shapes:
+    # TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env?
+    observation.images.cam_right_wrist: [3, 480, 640]
+    observation.images.cam_left_wrist: [3, 480, 640]
+    observation.images.cam_high: [3, 480, 640]
+    observation.images.cam_low: [3, 480, 640]
+  output_shapes:
+    action: ["${env.action_dim}"]
+
+  # Normalization / Unnormalization
+  input_normalization_modes:
+    observation.images.cam_right_wrist: mean_std
+    observation.images.cam_left_wrist: mean_std
+    observation.images.cam_high: mean_std
+    observation.images.cam_low: mean_std
+  output_normalization_modes:
+    action: mean_std
+
+  # Architecture.
+  # Vision backbone.
+  vision_backbone: resnet18
+  pretrained_backbone_weights: ResNet18_Weights.IMAGENET1K_V1
+  replace_final_stride_with_dilation: false
+  # Transformer layers.
+  pre_norm: false
+  dim_model: 512
+  n_heads: 8
+  dim_feedforward: 3200
+  feedforward_activation: relu
+  n_encoder_layers: 4
+  # Note: Although the original ACT implementation has 7 for `n_decoder_layers`, there is a bug in the code
+  # that means only the first layer is used. Here we match the original implementation by setting this to 1.
+  # See this issue https://github.com/tonyzhaozh/act/issues/25#issue-2258740521.
+  n_decoder_layers: 1
+  # VAE.
+  use_vae: true
+  latent_dim: 32
+  n_vae_encoder_layers: 4
+
+  # Inference.
+  temporal_ensemble_momentum: null
+
+  # Training and loss computation.
+  dropout: 0.1
+  kl_weight: 10.0

lerobot/configs/policy/diffusion.yaml

@@ -44,6 +44,10 @@ training:
     observation.state: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1)]"
     action: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1 - ${policy.n_obs_steps} + ${policy.horizon})]"
 
+  # The original implementation doesn't sample frames for the last 7 steps,
+  # which avoids excessive padding and leads to improved training results.
+  drop_n_last_frames: 7  # ${policy.horizon} - ${policy.n_action_steps} - ${policy.n_obs_steps} + 1
+
 eval:
   n_episodes: 50
   batch_size: 50

lerobot/scripts/push_dataset_to_hub.py

@@ -71,9 +71,9 @@ import torch
 from huggingface_hub import HfApi
 from safetensors.torch import save_file
 
+from lerobot.common.datasets.compute_stats import compute_stats
 from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
 from lerobot.common.datasets.push_dataset_to_hub._download_raw import download_raw
-from lerobot.common.datasets.push_dataset_to_hub.compute_stats import compute_stats
 from lerobot.common.datasets.utils import flatten_dict
 
 
@@ -86,6 +86,8 @@ def get_from_raw_to_lerobot_format_fn(raw_format):
         from lerobot.common.datasets.push_dataset_to_hub.aloha_hdf5_format import from_raw_to_lerobot_format
     elif raw_format == "aloha_dora":
         from lerobot.common.datasets.push_dataset_to_hub.aloha_dora_format import from_raw_to_lerobot_format
+    elif raw_format == "reachy2_hdf5":
+        from lerobot.common.datasets.push_dataset_to_hub.reachy2_hdf5_format import from_raw_to_lerobot_format
     elif raw_format == "xarm_pkl":
         from lerobot.common.datasets.push_dataset_to_hub.xarm_pkl_format import from_raw_to_lerobot_format
     else:

lerobot/scripts/train.py

@@ -16,7 +16,6 @@
 import logging
 import time
 from contextlib import nullcontext
-from copy import deepcopy
 from pathlib import Path
 from pprint import pformat
 
@@ -28,6 +27,8 @@ from termcolor import colored
 from torch.cuda.amp import GradScaler
 
 from lerobot.common.datasets.factory import make_dataset, resolve_delta_timestamps
+from lerobot.common.datasets.lerobot_dataset import MultiLeRobotDataset
+from lerobot.common.datasets.sampler import EpisodeAwareSampler
 from lerobot.common.datasets.utils import cycle
 from lerobot.common.envs.factory import make_env
 from lerobot.common.logger import Logger, log_output_dir
@@ -106,7 +107,7 @@ def update_policy(
     with torch.autocast(device_type=device.type) if use_amp else nullcontext():
         output_dict = policy.forward(batch)
         # TODO(rcadene): policy.unnormalize_outputs(out_dict)
-        loss = output_dict["loss"]
+        loss = output_dict["loss"].mean()
     grad_scaler.scale(loss).backward()
 
     # Unscale the graident of the optimzer's assigned params in-place **prior to gradient clipping**.
@@ -280,6 +281,11 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
 
     logging.info("make_dataset")
     offline_dataset = make_dataset(cfg)
+    if isinstance(offline_dataset, MultiLeRobotDataset):
+        logging.info(
+            "Multiple datasets were provided. Applied the following index mapping to the provided datasets: "
+            f"{pformat(offline_dataset.repo_id_to_index , indent=2)}"
+        )
 
     # Create environment used for evaluating checkpoints during training on simulation data.
     # On real-world data, no need to create an environment as evaluations are done outside train.py,
@@ -330,7 +336,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
                     max_episodes_rendered=4,
                     start_seed=cfg.seed,
                 )
-            log_eval_info(logger, eval_info["aggregated"], step, cfg, offline_dataset, is_offline)
+            log_eval_info(logger, eval_info["aggregated"], step, cfg, offline_dataset, is_offline=True)
             if cfg.wandb.enable:
                 logger.log_video(eval_info["video_paths"][0], step, mode="eval")
             logging.info("Resume training")
@@ -351,18 +357,28 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
             logging.info("Resume training")
 
     # create dataloader for offline training
+    if cfg.training.get("drop_n_last_frames"):
+        shuffle = False
+        sampler = EpisodeAwareSampler(
+            offline_dataset.episode_data_index,
+            drop_n_last_frames=cfg.training.drop_n_last_frames,
+            shuffle=True,
+        )
+    else:
+        shuffle = True
+        sampler = None
     dataloader = torch.utils.data.DataLoader(
         offline_dataset,
         num_workers=cfg.training.num_workers,
         batch_size=cfg.training.batch_size,
-        shuffle=True,
+        shuffle=shuffle,
+        sampler=sampler,
         pin_memory=device.type != "cpu",
         drop_last=False,
     )
     dl_iter = cycle(dataloader)
 
     policy.train()
-    is_offline = True
     for _ in range(step, cfg.training.offline_steps):
         if step == 0:
             logging.info("Start offline training on a fixed dataset")
@@ -382,7 +398,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
         )
 
         if step % cfg.training.log_freq == 0:
-            log_train_info(logger, train_info, step, cfg, offline_dataset, is_offline)
+            log_train_info(logger, train_info, step, cfg, offline_dataset, is_offline=True)
 
         # Note: evaluate_and_checkpoint_if_needed happens **after** the `step`th training update has completed,
         # so we pass in step + 1.
@@ -390,41 +406,8 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
 
         step += 1
 
-    logging.info("End of offline training")
-
-    if cfg.training.online_steps == 0:
-        if cfg.training.eval_freq > 0:
-            eval_env.close()
-        return
-
-    # create an env dedicated to online episodes collection from policy rollout
-    online_training_env = make_env(cfg, n_envs=1)
-
-    # create an empty online dataset similar to offline dataset
-    online_dataset = deepcopy(offline_dataset)
-    online_dataset.hf_dataset = {}
-    online_dataset.episode_data_index = {}
-
-    # create dataloader for online training
-    concat_dataset = torch.utils.data.ConcatDataset([offline_dataset, online_dataset])
-    weights = [1.0] * len(concat_dataset)
-    sampler = torch.utils.data.WeightedRandomSampler(
-        weights, num_samples=len(concat_dataset), replacement=True
-    )
-    dataloader = torch.utils.data.DataLoader(
-        concat_dataset,
-        num_workers=4,
-        batch_size=cfg.training.batch_size,
-        sampler=sampler,
-        pin_memory=device.type != "cpu",
-        drop_last=False,
-    )
-
-    logging.info("End of online training")
-
-    if cfg.training.eval_freq > 0:
-        eval_env.close()
-    online_training_env.close()
+    eval_env.close()
+    logging.info("End of training")
 
 
 @hydra.main(version_base="1.2", config_name="default", config_path="../configs")

lerobot/scripts/visualize_dataset.py

@@ -30,48 +30,46 @@ Examples:
 - Visualize data stored on a local machine:
 ```
 local$ python lerobot/scripts/visualize_dataset.py \
-    --repo-id lerobot/pusht \
-    --episode-index 0
+    --repo-id lerobot/pusht
+
+local$ open http://localhost:9090
 ```
 
 - Visualize data stored on a distant machine with a local viewer:
 ```
 distant$ python lerobot/scripts/visualize_dataset.py \
+    --repo-id lerobot/pusht
+
+local$ ssh -L 9090:localhost:9090 distant  # create a ssh tunnel
+local$ open http://localhost:9090
+```
+
+- Select episodes to visualize:
+```
+python lerobot/scripts/visualize_dataset.py \
     --repo-id lerobot/pusht \
-    --episode-index 0 \
-    --save 1 \
-    --output-dir path/to/directory
-
-local$ scp distant:path/to/directory/lerobot_pusht_episode_0.rrd .
-local$ rerun lerobot_pusht_episode_0.rrd
+    --episode-indices 7 3 5 1 4
 ```
-
-- Visualize data stored on a distant machine through streaming:
-(You need to forward the websocket port to the distant machine, with
-`ssh -L 9087:localhost:9087 username@remote-host`)
-```
-distant$ python lerobot/scripts/visualize_dataset.py \
-    --repo-id lerobot/pusht \
-    --episode-index 0 \
-    --mode distant \
-    --ws-port 9087
-
-local$ rerun ws://localhost:9087
-```
-
 """
 
 import argparse
-import gc
+import http.server
 import logging
-import time
+import os
+import shutil
+import socketserver
 from pathlib import Path
 
-import rerun as rr
 import torch
 import tqdm
+import yaml
+from bs4 import BeautifulSoup
+from huggingface_hub import snapshot_download
+from safetensors.torch import load_file, save_file
 
 from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.policies.act.modeling_act import ACTPolicy
+from lerobot.common.utils.utils import init_logging
 
 
 class EpisodeSampler(torch.utils.data.Sampler):
@@ -87,33 +85,307 @@ class EpisodeSampler(torch.utils.data.Sampler):
         return len(self.frame_ids)
 
 
-def to_hwc_uint8_numpy(chw_float32_torch):
-    assert chw_float32_torch.dtype == torch.float32
-    assert chw_float32_torch.ndim == 3
-    c, h, w = chw_float32_torch.shape
-    assert c < h and c < w, f"expect channel first images, but instead {chw_float32_torch.shape}"
-    hwc_uint8_numpy = (chw_float32_torch * 255).type(torch.uint8).permute(1, 2, 0).numpy()
-    return hwc_uint8_numpy
+class NoCacheHTTPRequestHandler(http.server.SimpleHTTPRequestHandler):
+    def end_headers(self):
+        self.send_header("Cache-Control", "no-store, no-cache, must-revalidate")
+        self.send_header("Pragma", "no-cache")
+        self.send_header("Expires", "0")
+        super().end_headers()
 
 
-def visualize_dataset(
-    repo_id: str,
-    episode_index: int,
-    batch_size: int = 32,
-    num_workers: int = 0,
-    mode: str = "local",
-    web_port: int = 9090,
-    ws_port: int = 9087,
-    save: bool = False,
-    output_dir: Path | None = None,
-) -> Path | None:
-    if save:
-        assert (
-            output_dir is not None
-        ), "Set an output directory where to write .rrd files with `--output-dir path/to/directory`."
+def run_server(path, port):
+    # Change directory to serve 'index.html` as front page
+    os.chdir(path)
 
-    logging.info("Loading dataset")
-    dataset = LeRobotDataset(repo_id)
+    with socketserver.TCPServer(("", port), NoCacheHTTPRequestHandler) as httpd:
+        logging.info(f"Serving HTTP on 0.0.0.0 port {port} (http://0.0.0.0:{port}/) ...")
+        httpd.serve_forever()
+
+
+def create_html_page(page_title: str):
+    """Create a html page with beautiful soop with default doctype, meta, header and title."""
+    soup = BeautifulSoup("", "html.parser")
+
+    doctype = soup.new_tag("!DOCTYPE html")
+    soup.append(doctype)
+
+    html = soup.new_tag("html", lang="en")
+    soup.append(html)
+
+    head = soup.new_tag("head")
+    html.append(head)
+
+    meta_charset = soup.new_tag("meta", charset="UTF-8")
+    head.append(meta_charset)
+
+    meta_viewport = soup.new_tag(
+        "meta", attrs={"name": "viewport", "content": "width=device-width, initial-scale=1.0"}
+    )
+    head.append(meta_viewport)
+
+    title = soup.new_tag("title")
+    title.string = page_title
+    head.append(title)
+
+    body = soup.new_tag("body")
+    html.append(body)
+
+    main_div = soup.new_tag("div")
+    body.append(main_div)
+    return soup, head, body
+
+
+def write_episode_data_csv(output_dir, file_name, episode_index, dataset, inference_results=None):
+    """Write a csv file containg timeseries data of an episode (e.g. state and action).
+    This file will be loaded by Dygraph javascript to plot data in real time."""
+    from_idx = dataset.episode_data_index["from"][episode_index]
+    to_idx = dataset.episode_data_index["to"][episode_index]
+
+    has_state = "observation.state" in dataset.hf_dataset.features
+    has_action = "action" in dataset.hf_dataset.features
+    has_inference = inference_results is not None
+
+    # init header of csv with state and action names
+    header = ["timestamp"]
+    if has_state:
+        dim_state = len(dataset.hf_dataset["observation.state"][0])
+        header += [f"state_{i}" for i in range(dim_state)]
+    if has_action:
+        dim_action = len(dataset.hf_dataset["action"][0])
+        header += [f"action_{i}" for i in range(dim_action)]
+    if has_inference:
+        assert "actions" in inference_results
+        assert "loss" in inference_results
+        dim_pred_action = inference_results["actions"].shape[2]
+        header += [f"pred_action_{i}" for i in range(dim_pred_action)]
+        header += ["loss"]
+
+    columns = ["timestamp"]
+    if has_state:
+        columns += ["observation.state"]
+    if has_action:
+        columns += ["action"]
+
+    rows = []
+    data = dataset.hf_dataset.select_columns(columns)
+    for i in range(from_idx, to_idx):
+        row = [data[i]["timestamp"].item()]
+        if has_state:
+            row += data[i]["observation.state"].tolist()
+        if has_action:
+            row += data[i]["action"].tolist()
+        rows.append(row)
+
+    if has_inference:
+        num_frames = len(rows)
+        assert num_frames == inference_results["actions"].shape[0]
+        assert num_frames == inference_results["loss"].shape[0]
+        for i in range(num_frames):
+            rows[i] += inference_results["actions"][i, 0].tolist()
+            rows[i] += [inference_results["loss"][i].item()]
+
+    output_dir.mkdir(parents=True, exist_ok=True)
+    with open(output_dir / file_name, "w") as f:
+        f.write(",".join(header) + "\n")
+        for row in rows:
+            row_str = [str(col) for col in row]
+            f.write(",".join(row_str) + "\n")
+
+
+def write_episode_data_js(output_dir, file_name, ep_csv_fname, dataset):
+    """Write a javascript file containing logic to synchronize camera feeds and timeseries."""
+    s = ""
+    s += "document.addEventListener('DOMContentLoaded', function () {\n"
+    for i, key in enumerate(dataset.video_frame_keys):
+        s += f"  const video{i} = document.getElementById('video_{key}');\n"
+    s += "  const slider = document.getElementById('videoControl');\n"
+    s += "  const playButton = document.getElementById('playButton');\n"
+    s += f"  const dygraph = new Dygraph(document.getElementById('graph'), '{ep_csv_fname}', " + "{\n"
+    s += "    pixelsPerPoint: 0.01,\n"
+    s += "    legend: 'always',\n"
+    s += "    labelsDiv: document.getElementById('labels'),\n"
+    s += "    labelsSeparateLines: true,\n"
+    s += "    labelsKMB: true,\n"
+    s += "    highlightCircleSize: 1.5,\n"
+    s += "    highlightSeriesOpts: {\n"
+    s += "        strokeWidth: 1.5,\n"
+    s += "        strokeBorderWidth: 1,\n"
+    s += "        highlightCircleSize: 3\n"
+    s += "    }\n"
+    s += "  });\n"
+    s += "\n"
+    s += "  // Function to play both videos\n"
+    s += "  playButton.addEventListener('click', function () {\n"
+    for i in range(len(dataset.video_frame_keys)):
+        s += f"    video{i}.play();\n"
+    s += "    // playButton.disabled = true; // Optional: disable button after playing\n"
+    s += "  });\n"
+    s += "\n"
+    s += "  // Update the video time when the slider value changes\n"
+    s += "  slider.addEventListener('input', function () {\n"
+    s += "    const sliderValue = slider.value;\n"
+    for i in range(len(dataset.video_frame_keys)):
+        s += f"    const time{i} = (video{i}.duration * sliderValue) / 100;\n"
+    for i in range(len(dataset.video_frame_keys)):
+        s += f"    video{i}.currentTime = time{i};\n"
+    s += "  });\n"
+    s += "\n"
+    s += "  // Synchronize slider with the video's current time\n"
+    s += "  const syncSlider = (video) => {\n"
+    s += "    video.addEventListener('timeupdate', function () {\n"
+    s += "      if (video.duration) {\n"
+    s += "        const pc = (100 / video.duration) * video.currentTime;\n"
+    s += "        slider.value = pc;\n"
+    s += "        const index = Math.floor(pc * dygraph.numRows() / 100);\n"
+    s += "        dygraph.setSelection(index, undefined, true, true);\n"
+    s += "      }\n"
+    s += "    });\n"
+    s += "  };\n"
+    s += "\n"
+    for i in range(len(dataset.video_frame_keys)):
+        s += f"  syncSlider(video{i});\n"
+    s += "\n"
+    s += "});\n"
+
+    output_dir.mkdir(parents=True, exist_ok=True)
+    with open(output_dir / file_name, "w", encoding="utf-8") as f:
+        f.write(s)
+
+
+def write_episode_data_html(output_dir, file_name, js_fname, ep_index, dataset):
+    """Write an html file containg video feeds and timeseries associated to an episode."""
+    soup, head, body = create_html_page("")
+
+    css_style = soup.new_tag("style")
+    css_style.string = ""
+    css_style.string += "#labels > span.highlight {\n"
+    css_style.string += "  border: 1px solid grey;\n"
+    css_style.string += "}"
+    head.append(css_style)
+
+    # Add videos from camera feeds
+
+    videos_control_div = soup.new_tag("div")
+    body.append(videos_control_div)
+
+    videos_div = soup.new_tag("div")
+    videos_control_div.append(videos_div)
+
+    def create_video(id, src):
+        video = soup.new_tag("video", id=id, width="320", height="240", controls="")
+        source = soup.new_tag("source", src=src, type="video/mp4")
+        video.string = "Your browser does not support the video tag."
+        video.append(source)
+        return video
+
+    # get first frame of episode (hack to get video_path of the episode)
+    first_frame_idx = dataset.episode_data_index["from"][ep_index].item()
+
+    for key in dataset.video_frame_keys:
+        # Example of video_path: 'videos/observation.image_episode_000004.mp4'
+        video_path = dataset.hf_dataset.select_columns(key)[first_frame_idx][key]["path"]
+        videos_div.append(create_video(f"video_{key}", video_path))
+
+    # Add controls for videos and graph
+
+    control_div = soup.new_tag("div")
+    videos_control_div.append(control_div)
+
+    button_div = soup.new_tag("div")
+    control_div.append(button_div)
+
+    button = soup.new_tag("button", id="playButton")
+    button.string = "Play Videos"
+    button_div.append(button)
+
+    slider_div = soup.new_tag("div")
+    control_div.append(slider_div)
+
+    slider = soup.new_tag("input", type="range", id="videoControl", min="0", max="100", value="0", step="1")
+    control_div.append(slider)
+
+    # Add graph of states/actions, and its labels
+
+    graph_labels_div = soup.new_tag("div", style="display: flex;")
+    body.append(graph_labels_div)
+
+    graph_div = soup.new_tag("div", id="graph", style="flex: 1; width: 85%")
+    graph_labels_div.append(graph_div)
+
+    labels_div = soup.new_tag("div", id="labels", style="flex: 1; width: 15%")
+    graph_labels_div.append(labels_div)
+
+    # add dygraph library
+    script = soup.new_tag("script", type="text/javascript", src=js_fname)
+    body.append(script)
+
+    script_dygraph = soup.new_tag(
+        "script",
+        type="text/javascript",
+        src="https://cdn.jsdelivr.net/npm/dygraphs@2.1.0/dist/dygraph.min.js",
+    )
+    body.append(script_dygraph)
+
+    link_dygraph = soup.new_tag(
+        "link", rel="stylesheet", href="https://cdn.jsdelivr.net/npm/dygraphs@2.1.0/dist/dygraph.min.css"
+    )
+    body.append(link_dygraph)
+
+    # Write as a html file
+
+    output_dir.mkdir(parents=True, exist_ok=True)
+    with open(output_dir / file_name, "w", encoding="utf-8") as f:
+        f.write(soup.prettify())
+
+
+def write_episodes_list_html(output_dir, file_name, ep_indices, ep_html_fnames, dataset):
+    """Write an html file containing information related to the dataset and a list of links to
+    html pages of episodes."""
+    soup, head, body = create_html_page("TODO")
+
+    h3 = soup.new_tag("h3")
+    h3.string = "TODO"
+    body.append(h3)
+
+    ul_info = soup.new_tag("ul")
+    body.append(ul_info)
+
+    li_info = soup.new_tag("li")
+    li_info.string = f"Number of samples/frames: {dataset.num_samples}"
+    ul_info.append(li_info)
+
+    li_info = soup.new_tag("li")
+    li_info.string = f"Number of episodes: {dataset.num_episodes}"
+    ul_info.append(li_info)
+
+    li_info = soup.new_tag("li")
+    li_info.string = f"Frames per second: {dataset.fps}"
+    ul_info.append(li_info)
+
+    # li_info = soup.new_tag("li")
+    # li_info.string = f"Size: {format_big_number(dataset.hf_dataset.info.size_in_bytes)}B"
+    # ul_info.append(li_info)
+
+    ul = soup.new_tag("ul")
+    body.append(ul)
+
+    for ep_idx, ep_html_fname in zip(ep_indices, ep_html_fnames, strict=False):
+        li = soup.new_tag("li")
+        ul.append(li)
+
+        a = soup.new_tag("a", href=ep_html_fname)
+        a.string = f"Episode number {ep_idx}"
+
+        li.append(a)
+
+    output_dir.mkdir(parents=True, exist_ok=True)
+    with open(output_dir / file_name, "w", encoding="utf-8") as f:
+        f.write(soup.prettify())
+
+
+def run_inference(dataset, episode_index, policy, num_workers=4, batch_size=32, device="cuda"):
+    policy.eval()
+    policy.to(device)
 
     logging.info("Loading dataloader")
     episode_sampler = EpisodeSampler(dataset, episode_index)
@@ -124,70 +396,104 @@ def visualize_dataset(
         sampler=episode_sampler,
     )
 
-    logging.info("Starting Rerun")
-
-    if mode not in ["local", "distant"]:
-        raise ValueError(mode)
-
-    spawn_local_viewer = mode == "local" and not save
-    rr.init(f"{repo_id}/episode_{episode_index}", spawn=spawn_local_viewer)
-
-    # Manually call python garbage collector after `rr.init` to avoid hanging in a blocking flush
-    # when iterating on a dataloader with `num_workers` > 0
-    # TODO(rcadene): remove `gc.collect` when rerun version 0.16 is out, which includes a fix
-    gc.collect()
-
-    if mode == "distant":
-        rr.serve(open_browser=False, web_port=web_port, ws_port=ws_port)
-
-    logging.info("Logging to Rerun")
-
+    logging.info("Running inference")
+    inference_results = {}
     for batch in tqdm.tqdm(dataloader, total=len(dataloader)):
-        # iterate over the batch
-        for i in range(len(batch["index"])):
-            rr.set_time_sequence("frame_index", batch["frame_index"][i].item())
-            rr.set_time_seconds("timestamp", batch["timestamp"][i].item())
+        batch = {k: v.to(device, non_blocking=True) for k, v in batch.items()}
+        with torch.inference_mode():
+            output_dict = policy.forward(batch)
 
-            # display each camera image
-            for key in dataset.camera_keys:
-                # TODO(rcadene): add `.compress()`? is it lossless?
-                rr.log(key, rr.Image(to_hwc_uint8_numpy(batch[key][i])))
+        for key in output_dict:
+            if key not in inference_results:
+                inference_results[key] = []
+            inference_results[key].append(output_dict[key].to("cpu"))
 
-            # display each dimension of action space (e.g. actuators command)
-            if "action" in batch:
-                for dim_idx, val in enumerate(batch["action"][i]):
-                    rr.log(f"action/{dim_idx}", rr.Scalar(val.item()))
+    for key in inference_results:
+        inference_results[key] = torch.cat(inference_results[key])
 
-            # display each dimension of observed state space (e.g. agent position in joint space)
-            if "observation.state" in batch:
-                for dim_idx, val in enumerate(batch["observation.state"][i]):
-                    rr.log(f"state/{dim_idx}", rr.Scalar(val.item()))
+    return inference_results
 
-            if "next.done" in batch:
-                rr.log("next.done", rr.Scalar(batch["next.done"][i].item()))
 
-            if "next.reward" in batch:
-                rr.log("next.reward", rr.Scalar(batch["next.reward"][i].item()))
+def visualize_dataset(
+    repo_id: str,
+    episode_indices: list[int] = None,
+    output_dir: Path | None = None,
+    serve: bool = True,
+    port: int = 9090,
+    force_overwrite: bool = True,
+    policy_repo_id: str | None = None,
+    policy_ckpt_path: Path | None = None,
+    batch_size: int = 32,
+    num_workers: int = 4,
+) -> Path | None:
+    init_logging()
 
-            if "next.success" in batch:
-                rr.log("next.success", rr.Scalar(batch["next.success"][i].item()))
+    has_policy = policy_repo_id or policy_ckpt_path
 
-    if mode == "local" and save:
-        # save .rrd locally
-        output_dir = Path(output_dir)
-        output_dir.mkdir(parents=True, exist_ok=True)
-        repo_id_str = repo_id.replace("/", "_")
-        rrd_path = output_dir / f"{repo_id_str}_episode_{episode_index}.rrd"
-        rr.save(rrd_path)
-        return rrd_path
+    if has_policy:
+        logging.info("Loading policy")
+        if policy_repo_id:
+            pretrained_policy_path = Path(snapshot_download(policy_repo_id))
+        elif policy_ckpt_path:
+            pretrained_policy_path = Path(policy_ckpt_path)
+        policy = ACTPolicy.from_pretrained(pretrained_policy_path)
+        with open(pretrained_policy_path / "config.yaml") as f:
+            cfg = yaml.safe_load(f)
+        delta_timestamps = cfg["training"]["delta_timestamps"]
+    else:
+        delta_timestamps = None
 
-    elif mode == "distant":
-        # stop the process from exiting since it is serving the websocket connection
-        try:
-            while True:
-                time.sleep(1)
-        except KeyboardInterrupt:
-            print("Ctrl-C received. Exiting.")
+    logging.info("Loading dataset")
+    dataset = LeRobotDataset(repo_id, delta_timestamps=delta_timestamps)
+
+    if not dataset.video:
+        raise NotImplementedError(f"Image datasets ({dataset.video=}) are currently not supported.")
+
+    if output_dir is None:
+        output_dir = f"outputs/visualize_dataset/{repo_id}"
+
+    output_dir = Path(output_dir)
+    if force_overwrite and output_dir.exists():
+        shutil.rmtree(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # Create a simlink from the dataset video folder containg mp4 files to the output directory
+    # so that the http server can get access to the mp4 files.
+    ln_videos_dir = output_dir / "videos"
+    if not ln_videos_dir.exists():
+        ln_videos_dir.symlink_to(dataset.videos_dir.resolve())
+
+    if episode_indices is None:
+        episode_indices = list(range(dataset.num_episodes))
+
+    logging.info("Writing html")
+    ep_html_fnames = []
+    for episode_index in tqdm.tqdm(episode_indices):
+        inference_results = None
+        if has_policy:
+            inference_results_path = output_dir / f"episode_{episode_index}.safetensors"
+            if inference_results_path.exists():
+                inference_results = load_file(inference_results_path)
+            else:
+                inference_results = run_inference(dataset, episode_index, policy)
+                save_file(inference_results, inference_results_path)
+
+        # write states and actions in a csv
+        ep_csv_fname = f"episode_{episode_index}.csv"
+        write_episode_data_csv(output_dir, ep_csv_fname, episode_index, dataset, inference_results)
+
+        js_fname = f"episode_{episode_index}.js"
+        write_episode_data_js(output_dir, js_fname, ep_csv_fname, dataset)
+
+        # write a html page to view videos and timeseries
+        ep_html_fname = f"episode_{episode_index}.html"
+        write_episode_data_html(output_dir, ep_html_fname, js_fname, episode_index, dataset)
+        ep_html_fnames.append(ep_html_fname)
+
+    write_episodes_list_html(output_dir, "index.html", episode_indices, ep_html_fnames, dataset)
+
+    if serve:
+        run_server(output_dir, port)
 
 
 def main():
@@ -197,13 +503,51 @@ def main():
         "--repo-id",
         type=str,
         required=True,
-        help="Name of hugging face repositery containing a LeRobotDataset dataset (e.g. `lerobot/pusht`).",
+        help="Name of hugging face repositery containing a LeRobotDataset dataset (e.g. `lerobot/pusht` for https://huggingface.co/datasets/lerobot/pusht).",
     )
     parser.add_argument(
-        "--episode-index",
+        "--episode-indices",
         type=int,
-        required=True,
-        help="Episode to visualize.",
+        nargs="*",
+        default=None,
+        help="Episode indices to visualize (e.g. `0 1 5 6` to load episodes of index 0, 1, 5 and 6). By default loads all episodes.",
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=str,
+        default=None,
+        help="Directory path to write html files and kickoff a web server. By default write them to 'outputs/visualize_dataset/REPO_ID'.",
+    )
+    parser.add_argument(
+        "--serve",
+        type=int,
+        default=1,
+        help="Launch web server.",
+    )
+    parser.add_argument(
+        "--port",
+        type=int,
+        default=9090,
+        help="Web port used by the http server.",
+    )
+    parser.add_argument(
+        "--force-overwrite",
+        type=int,
+        default=1,
+        help="Delete the output directory if it exists already.",
+    )
+
+    parser.add_argument(
+        "--policy-repo-id",
+        type=str,
+        default=None,
+        help="Name of hugging face repositery containing a pretrained policy (e.g. `lerobot/diffusion_pusht` for https://huggingface.co/lerobot/diffusion_pusht).",
+    )
+    parser.add_argument(
+        "--policy-ckpt-path",
+        type=str,
+        default=None,
+        help="Name of hugging face repositery containing a pretrained policy (e.g. `lerobot/diffusion_pusht` for https://huggingface.co/lerobot/diffusion_pusht).",
     )
     parser.add_argument(
         "--batch-size",
@@ -217,43 +561,6 @@ def main():
         default=4,
         help="Number of processes of Dataloader for loading the data.",
     )
-    parser.add_argument(
-        "--mode",
-        type=str,
-        default="local",
-        help=(
-            "Mode of viewing between 'local' or 'distant'. "
-            "'local' requires data to be on a local machine. It spawns a viewer to visualize the data locally. "
-            "'distant' creates a server on the distant machine where the data is stored. Visualize the data by connecting to the server with `rerun ws://localhost:PORT` on the local machine."
-        ),
-    )
-    parser.add_argument(
-        "--web-port",
-        type=int,
-        default=9090,
-        help="Web port for rerun.io when `--mode distant` is set.",
-    )
-    parser.add_argument(
-        "--ws-port",
-        type=int,
-        default=9087,
-        help="Web socket port for rerun.io when `--mode distant` is set.",
-    )
-    parser.add_argument(
-        "--save",
-        type=int,
-        default=0,
-        help=(
-            "Save a .rrd file in the directory provided by `--output-dir`. "
-            "It also deactivates the spawning of a viewer. ",
-            "Visualize the data by running `rerun path/to/file.rrd` on your local machine.",
-        ),
-    )
-    parser.add_argument(
-        "--output-dir",
-        type=str,
-        help="Directory path to write a .rrd file when `--save 1` is set.",
-    )
 
     args = parser.parse_args()
     visualize_dataset(**vars(args))

lerobot/scripts/visualize_dataset_rerun.py

@@ -0,0 +1,263 @@
+#!/usr/bin/env python
+
+# Copyright 2024 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.
+""" Visualize data of **all** frames of any episode of a dataset of type LeRobotDataset.
+
+Note: The last frame of the episode doesnt always correspond to a final state.
+That's because our datasets are composed of transition from state to state up to
+the antepenultimate state associated to the ultimate action to arrive in the final state.
+However, there might not be a transition from a final state to another state.
+
+Note: This script aims to visualize the data used to train the neural networks.
+~What you see is what you get~. When visualizing image modality, it is often expected to observe
+lossly compression artifacts since these images have been decoded from compressed mp4 videos to
+save disk space. The compression factor applied has been tuned to not affect success rate.
+
+Examples:
+
+- Visualize data stored on a local machine:
+```
+local$ python lerobot/scripts/visualize_dataset.py \
+    --repo-id lerobot/pusht \
+    --episode-index 0
+```
+
+- Visualize data stored on a distant machine with a local viewer:
+```
+distant$ python lerobot/scripts/visualize_dataset.py \
+    --repo-id lerobot/pusht \
+    --episode-index 0 \
+    --save 1 \
+    --output-dir path/to/directory
+
+local$ scp distant:path/to/directory/lerobot_pusht_episode_0.rrd .
+local$ rerun lerobot_pusht_episode_0.rrd
+```
+
+- Visualize data stored on a distant machine through streaming:
+(You need to forward the websocket port to the distant machine, with
+`ssh -L 9087:localhost:9087 username@remote-host`)
+```
+distant$ python lerobot/scripts/visualize_dataset.py \
+    --repo-id lerobot/pusht \
+    --episode-index 0 \
+    --mode distant \
+    --ws-port 9087
+
+local$ rerun ws://localhost:9087
+```
+
+"""
+
+import argparse
+import gc
+import logging
+import time
+from pathlib import Path
+
+import rerun as rr
+import torch
+import tqdm
+
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+
+
+class EpisodeSampler(torch.utils.data.Sampler):
+    def __init__(self, dataset, episode_index):
+        from_idx = dataset.episode_data_index["from"][episode_index].item()
+        to_idx = dataset.episode_data_index["to"][episode_index].item()
+        self.frame_ids = range(from_idx, to_idx)
+
+    def __iter__(self):
+        return iter(self.frame_ids)
+
+    def __len__(self):
+        return len(self.frame_ids)
+
+
+def to_hwc_uint8_numpy(chw_float32_torch):
+    assert chw_float32_torch.dtype == torch.float32
+    assert chw_float32_torch.ndim == 3
+    c, h, w = chw_float32_torch.shape
+    assert c < h and c < w, f"expect channel first images, but instead {chw_float32_torch.shape}"
+    hwc_uint8_numpy = (chw_float32_torch * 255).type(torch.uint8).permute(1, 2, 0).numpy()
+    return hwc_uint8_numpy
+
+
+def visualize_dataset(
+    repo_id: str,
+    episode_index: int,
+    batch_size: int = 32,
+    num_workers: int = 0,
+    mode: str = "local",
+    web_port: int = 9090,
+    ws_port: int = 9087,
+    save: bool = False,
+    output_dir: Path | None = None,
+) -> Path | None:
+    if save:
+        assert (
+            output_dir is not None
+        ), "Set an output directory where to write .rrd files with `--output-dir path/to/directory`."
+
+    logging.info("Loading dataset")
+    dataset = LeRobotDataset(repo_id)
+
+    logging.info("Loading dataloader")
+    episode_sampler = EpisodeSampler(dataset, episode_index)
+    dataloader = torch.utils.data.DataLoader(
+        dataset,
+        num_workers=num_workers,
+        batch_size=batch_size,
+        sampler=episode_sampler,
+    )
+
+    logging.info("Starting Rerun")
+
+    if mode not in ["local", "distant"]:
+        raise ValueError(mode)
+
+    spawn_local_viewer = mode == "local" and not save
+    rr.init(f"{repo_id}/episode_{episode_index}", spawn=spawn_local_viewer)
+
+    # Manually call python garbage collector after `rr.init` to avoid hanging in a blocking flush
+    # when iterating on a dataloader with `num_workers` > 0
+    # TODO(rcadene): remove `gc.collect` when rerun version 0.16 is out, which includes a fix
+    gc.collect()
+
+    if mode == "distant":
+        rr.serve(open_browser=False, web_port=web_port, ws_port=ws_port)
+
+    logging.info("Logging to Rerun")
+
+    for batch in tqdm.tqdm(dataloader, total=len(dataloader)):
+        # iterate over the batch
+        for i in range(len(batch["index"])):
+            rr.set_time_sequence("frame_index", batch["frame_index"][i].item())
+            rr.set_time_seconds("timestamp", batch["timestamp"][i].item())
+
+            # display each camera image
+            for key in dataset.camera_keys:
+                # TODO(rcadene): add `.compress()`? is it lossless?
+                rr.log(key, rr.Image(to_hwc_uint8_numpy(batch[key][i])))
+
+            # display each dimension of action space (e.g. actuators command)
+            if "action" in batch:
+                for dim_idx, val in enumerate(batch["action"][i]):
+                    rr.log(f"action/{dim_idx}", rr.Scalar(val.item()))
+
+            # display each dimension of observed state space (e.g. agent position in joint space)
+            if "observation.state" in batch:
+                for dim_idx, val in enumerate(batch["observation.state"][i]):
+                    rr.log(f"state/{dim_idx}", rr.Scalar(val.item()))
+
+            if "next.done" in batch:
+                rr.log("next.done", rr.Scalar(batch["next.done"][i].item()))
+
+            if "next.reward" in batch:
+                rr.log("next.reward", rr.Scalar(batch["next.reward"][i].item()))
+
+            if "next.success" in batch:
+                rr.log("next.success", rr.Scalar(batch["next.success"][i].item()))
+
+    if mode == "local" and save:
+        # save .rrd locally
+        output_dir = Path(output_dir)
+        output_dir.mkdir(parents=True, exist_ok=True)
+        repo_id_str = repo_id.replace("/", "_")
+        rrd_path = output_dir / f"{repo_id_str}_episode_{episode_index}.rrd"
+        rr.save(rrd_path)
+        return rrd_path
+
+    elif mode == "distant":
+        # stop the process from exiting since it is serving the websocket connection
+        try:
+            while True:
+                time.sleep(1)
+        except KeyboardInterrupt:
+            print("Ctrl-C received. Exiting.")
+
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--repo-id",
+        type=str,
+        required=True,
+        help="Name of hugging face repositery containing a LeRobotDataset dataset (e.g. `lerobot/pusht`).",
+    )
+    parser.add_argument(
+        "--episode-index",
+        type=int,
+        required=True,
+        help="Episode to visualize.",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=32,
+        help="Batch size loaded by DataLoader.",
+    )
+    parser.add_argument(
+        "--num-workers",
+        type=int,
+        default=4,
+        help="Number of processes of Dataloader for loading the data.",
+    )
+    parser.add_argument(
+        "--mode",
+        type=str,
+        default="local",
+        help=(
+            "Mode of viewing between 'local' or 'distant'. "
+            "'local' requires data to be on a local machine. It spawns a viewer to visualize the data locally. "
+            "'distant' creates a server on the distant machine where the data is stored. Visualize the data by connecting to the server with `rerun ws://localhost:PORT` on the local machine."
+        ),
+    )
+    parser.add_argument(
+        "--web-port",
+        type=int,
+        default=9090,
+        help="Web port for rerun.io when `--mode distant` is set.",
+    )
+    parser.add_argument(
+        "--ws-port",
+        type=int,
+        default=9087,
+        help="Web socket port for rerun.io when `--mode distant` is set.",
+    )
+    parser.add_argument(
+        "--save",
+        type=int,
+        default=0,
+        help=(
+            "Save a .rrd file in the directory provided by `--output-dir`. "
+            "It also deactivates the spawning of a viewer. ",
+            "Visualize the data by running `rerun path/to/file.rrd` on your local machine.",
+        ),
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=str,
+        help="Directory path to write a .rrd file when `--save 1` is set.",
+    )
+
+    args = parser.parse_args()
+    visualize_dataset(**vars(args))
+
+
+if __name__ == "__main__":
+    main()

poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -444,63 +444,63 @@ files = [
 
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+version = "7.5.3"
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 [package.dependencies]
@@ -785,6 +785,26 @@ files = [
 [package.dependencies]
 six = ">=1.4.0"
 
+[[package]]
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+version = "0.3.4"
+description = "`dora` goal is to be a low latency, composable, and distributed data flow."
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+python-versions = "*"
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+]
+
+[package.dependencies]
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+
 [[package]]
 name = "einops"
 version = "0.8.0"
@@ -1066,6 +1086,27 @@ mujoco = ">=2.3.7,<3.0.0"
 dev = ["debugpy (>=1.8.1)", "pre-commit (>=3.7.0)"]
 test = ["pytest (>=8.1.0)", "pytest-cov (>=5.0.0)"]
 
+[[package]]
+name = "gym-dora"
+version = "0.1.0"
+description = ""
+optional = true
+python-versions = "^3.10"
+files = []
+develop = false
+
+[package.dependencies]
+dora-rs = ">=0.3.4"
+gymnasium = ">=0.29.1"
+pyarrow = ">=12.0.0"
+
+[package.source]
+type = "git"
+url = "https://github.com/dora-rs/dora-lerobot.git"
+reference = "HEAD"
+resolved_reference = "ed0c00a4fdc6ec856c9842551acd7dc7ee776f79"
+subdirectory = "gym_dora"
+
 [[package]]
 name = "gym-pusht"
 version = "0.1.4"
@@ -1269,13 +1310,13 @@ files = [
 
 [[package]]
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+version = "0.23.2"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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@@ -2061,18 +2102,15 @@ test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
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 name = "nodeenv"
-version = "1.8.0"
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 description = "Node.js virtual environment builder"
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 files = [
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+    {file = "nodeenv-1.9.0-py2.py3-none-any.whl", hash = "sha256:508ecec98f9f3330b636d4448c0f1a56fc68017c68f1e7857ebc52acf0eb879a"},
+    {file = "nodeenv-1.9.0.tar.gz", hash = "sha256:07f144e90dae547bf0d4ee8da0ee42664a42a04e02ed68e06324348dafe4bdb1"},
 ]
 
-[package.dependencies]
-setuptools = "*"
-
 [[package]]
 name = "numba"
 version = "0.59.1"
@@ -2406,6 +2444,7 @@ optional = false
 python-versions = ">=3.9"
 files = [
     {file = "pandas-2.2.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:90c6fca2acf139569e74e8781709dccb6fe25940488755716d1d354d6bc58bce"},
+    {file = "pandas-2.2.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c7adfc142dac335d8c1e0dcbd37eb8617eac386596eb9e1a1b77791cf2498238"},
     {file = "pandas-2.2.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4abfe0be0d7221be4f12552995e58723c7422c80a659da13ca382697de830c08"},
     {file = "pandas-2.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8635c16bf3d99040fdf3ca3db669a7250ddf49c55dc4aa8fe0ae0fa8d6dcc1f0"},
     {file = "pandas-2.2.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:40ae1dffb3967a52203105a077415a86044a2bea011b5f321c6aa64b379a3f51"},
@@ -2426,6 +2465,7 @@ files = [
     {file = "pandas-2.2.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:43498c0bdb43d55cb162cdc8c06fac328ccb5d2eabe3cadeb3529ae6f0517c32"},
     {file = "pandas-2.2.2-cp312-cp312-win_amd64.whl", hash = "sha256:d187d355ecec3629624fccb01d104da7d7f391db0311145817525281e2804d23"},
     {file = "pandas-2.2.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:0ca6377b8fca51815f382bd0b697a0814c8bda55115678cbc94c30aacbb6eff2"},
+    {file = "pandas-2.2.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9057e6aa78a584bc93a13f0a9bf7e753a5e9770a30b4d758b8d5f2a62a9433cd"},
     {file = "pandas-2.2.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:001910ad31abc7bf06f49dcc903755d2f7f3a9186c0c040b827e522e9cef0863"},
     {file = "pandas-2.2.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:66b479b0bd07204e37583c191535505410daa8df638fd8e75ae1b383851fe921"},
     {file = "pandas-2.2.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:a77e9d1c386196879aa5eb712e77461aaee433e54c68cf253053a73b7e49c33a"},
@@ -3188,13 +3228,13 @@ files = [
 
 [[package]]
 name = "requests"
-version = "2.32.2"
+version = "2.32.3"
 description = "Python HTTP for Humans."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "requests-2.32.2-py3-none-any.whl", hash = "sha256:fc06670dd0ed212426dfeb94fc1b983d917c4f9847c863f313c9dfaaffb7c23c"},
-    {file = "requests-2.32.2.tar.gz", hash = "sha256:dd951ff5ecf3e3b3aa26b40703ba77495dab41da839ae72ef3c8e5d8e2433289"},
+    {file = "requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"},
+    {file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"},
 ]
 
 [package.dependencies]
@@ -3210,16 +3250,16 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "rerun-sdk"
-version = "0.16.0"
+version = "0.16.1"
 description = "The Rerun Logging SDK"
 optional = false
 python-versions = "<3.13,>=3.8"
 files = [
-    {file = "rerun_sdk-0.16.0-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:1cc6dc66d089e296f945dc238301889efb61dd6d338b5d00f76981cf7aed0a74"},
-    {file = "rerun_sdk-0.16.0-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:faf231897655e46eb975695df2b0ace07db362d697e697f9a3dff52f81c0dc5d"},
-    {file = "rerun_sdk-0.16.0-cp38-abi3-manylinux_2_31_aarch64.whl", hash = "sha256:860a6394380d3e9b9e48bf34423bd56dda54d5b0158d2ae0e433698659b34198"},
-    {file = "rerun_sdk-0.16.0-cp38-abi3-manylinux_2_31_x86_64.whl", hash = "sha256:5b8d1476f73a3ad1a5d3f21b61c633f3ab62aa80fa0b049f5ad10bf1227681ab"},
-    {file = "rerun_sdk-0.16.0-cp38-abi3-win_amd64.whl", hash = "sha256:aff0051a263b8c3067243c0126d319845baf4fe640899f04aeef7daf151f35e4"},
+    {file = "rerun_sdk-0.16.1-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:170c6976634008611753e10dfef8cdc395ce8180e634c169e7c61cef2f89a277"},
+    {file = "rerun_sdk-0.16.1-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:c9a76eab7eb5559276737dad655200e9350df0837158dbc5a896970ab4201454"},
+    {file = "rerun_sdk-0.16.1-cp38-abi3-manylinux_2_31_aarch64.whl", hash = "sha256:4d6436752d57e8b8038489a0e7e37f0c760b088e96db5fb81667d3a376d63fea"},
+    {file = "rerun_sdk-0.16.1-cp38-abi3-manylinux_2_31_x86_64.whl", hash = "sha256:37b7b47948471873e84f224b16f417a94a91c7cbd6c72c68281eeff1ba414b8f"},
+    {file = "rerun_sdk-0.16.1-cp38-abi3-win_amd64.whl", hash = "sha256:be88799c8afdf68eafa99e64e2e4f0a484e187e017a180219abbe6bb988acd4e"},
 ]
 
 [package.dependencies]
@@ -3696,17 +3736,17 @@ files = [
 
 [[package]]
 name = "sympy"
-version = "1.12"
+version = "1.12.1"
 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "sympy-1.12-py3-none-any.whl", hash = "sha256:c3588cd4295d0c0f603d0f2ae780587e64e2efeedb3521e46b9bb1d08d184fa5"},
-    {file = "sympy-1.12.tar.gz", hash = "sha256:ebf595c8dac3e0fdc4152c51878b498396ec7f30e7a914d6071e674d49420fb8"},
+    {file = "sympy-1.12.1-py3-none-any.whl", hash = "sha256:9b2cbc7f1a640289430e13d2a56f02f867a1da0190f2f99d8968c2f74da0e515"},
+    {file = "sympy-1.12.1.tar.gz", hash = "sha256:2877b03f998cd8c08f07cd0de5b767119cd3ef40d09f41c30d722f6686b0fb88"},
 ]
 
 [package.dependencies]
-mpmath = ">=0.19"
+mpmath = ">=1.1.0,<1.4.0"
 
 [[package]]
 name = "tbb"
@@ -4220,13 +4260,13 @@ multidict = ">=4.0"
 
 [[package]]
 name = "zarr"
-version = "2.18.1"
+version = "2.18.2"
 description = "An implementation of chunked, compressed, N-dimensional arrays for Python"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "zarr-2.18.1-py3-none-any.whl", hash = "sha256:a1770d194eec4ec0a41a01295a6f724e1c3471d704d3aca906d3b3a7f8830245"},
-    {file = "zarr-2.18.1.tar.gz", hash = "sha256:28c360ed123e606c425a694a83300227a907cb86a995fc9eef620ecafbe5f92d"},
+    {file = "zarr-2.18.2-py3-none-any.whl", hash = "sha256:a638754902f97efa99b406083fdc807a0e2ccf12a949117389d2a4ba9b05df38"},
+    {file = "zarr-2.18.2.tar.gz", hash = "sha256:9bb393b8a0a38fb121dbb913b047d75db28de9890f6d644a217a73cf4ae74f47"},
 ]
 
 [package.dependencies]
@@ -4241,13 +4281,13 @@ jupyter = ["ipytree (>=0.2.2)", "ipywidgets (>=8.0.0)", "notebook"]
 
 [[package]]
 name = "zipp"
-version = "3.18.2"
+version = "3.19.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.18.2-py3-none-any.whl", hash = "sha256:dce197b859eb796242b0622af1b8beb0a722d52aa2f57133ead08edd5bf5374e"},
-    {file = "zipp-3.18.2.tar.gz", hash = "sha256:6278d9ddbcfb1f1089a88fde84481528b07b0e10474e09dcfe53dad4069fa059"},
+    {file = "zipp-3.19.0-py3-none-any.whl", hash = "sha256:96dc6ad62f1441bcaccef23b274ec471518daf4fbbc580341204936a5a3dddec"},
+    {file = "zipp-3.19.0.tar.gz", hash = "sha256:952df858fb3164426c976d9338d3961e8e8b3758e2e059e0f754b8c4262625ee"},
 ]
 
 [package.extras]
@@ -4257,6 +4297,7 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more
 [extras]
 aloha = ["gym-aloha"]
 dev = ["debugpy", "pre-commit"]
+dora = ["gym-dora"]
 pusht = ["gym-pusht"]
 test = ["pytest", "pytest-cov"]
 umi = ["imagecodecs"]
@@ -4265,4 +4306,4 @@ xarm = ["gym-xarm"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "1ad6ef0f88f0056ab639e60e033e586f7460a9c5fc3676a477bbd47923f41cb6"
+content-hash = "23ddb8dd774a4faf85d08a07dfdf19badb7c370120834b71df4afca254520771"

pyproject.toml

@@ -46,6 +46,7 @@ h5py = ">=3.10.0"
 huggingface-hub = {extras = ["hf-transfer"], version = "^0.23.0"}
 gymnasium = ">=0.29.1"
 cmake = ">=3.29.0.1"
+gym-dora = { git = "https://github.com/dora-rs/dora-lerobot.git", subdirectory = "gym_dora", optional = true }
 gym-pusht = { version = ">=0.1.3", optional = true}
 gym-xarm = { version = ">=0.1.1", optional = true}
 gym-aloha = { version = ">=0.1.1", optional = true}
@@ -62,6 +63,7 @@ deepdiff = ">=7.0.1"
 
 
 [tool.poetry.extras]
+dora = ["gym-dora"]
 pusht = ["gym-pusht"]
 xarm = ["gym-xarm"]
 aloha = ["gym-aloha"]

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real/actions.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2fff6294b94cf42d4dd1249dcc5c3b0269d6d9c697f894e61b867d7ab81a94e4
+size 5104

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real/grad_stats.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4aa23e51607604a18b70fa42edbbe1af34f119d985628fc27cc1bbb0efbc8901
+size 31688

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real/output_dict.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6fd368406c93cb562a69ff11cf7adf34a4b223507dcb2b9e9b8f44ee1036988a
+size 68

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real/param_stats.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5663ee79a13bb70a1604b887dd21bf89d18482287442419c6cc6c5bf0e753e99
+size 34928

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real_no_state/actions.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fb1a45463efd860af2ca22c16c77d55a18bd96fef080ae77978845a2f22ef716
+size 5104

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real_no_state/grad_stats.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aa5a43e22f01d8e2f8d19f31753608794f1edbd74aaf71660091ab80ea58dc9b
+size 30808

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real_no_state/output_dict.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:97455b4360748c99905cd103473c1a52da6901d0a73ffbc51b5ea3eb250d1386
+size 68

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real_no_state/param_stats.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:54d1f75cf67a7b1d7a7c6865ecb9b1cc86a2f032d1890245f8996789ab6e0df6
+size 33608

tests/scripts/save_policy_to_safetensors.py

@@ -75,15 +75,16 @@ def get_policy_stats(env_name, policy_name, extra_overrides):
     # HACK: We reload a batch with no delta_timestamps as `select_action` won't expect a timestamps dimension
     dataset.delta_timestamps = None
     batch = next(iter(dataloader))
-    obs = {
-        k: batch[k]
-        for k in batch
-        if k in ["observation.image", "observation.images.top", "observation.state"]
-    }
+    obs = {}
+    for k in batch:
+        if k.startswith("observation"):
+            obs[k] = batch[k]
+
+    if "n_action_steps" in cfg.policy:
+        actions_queue = cfg.policy.n_action_steps
+    else:
+        actions_queue = cfg.policy.n_action_repeats
 
-    actions_queue = (
-        cfg.policy.n_action_steps if "n_action_steps" in cfg.policy else cfg.policy.n_action_repeats
-    )
     actions = {str(i): policy.select_action(obs).contiguous() for i in range(actions_queue)}
     return output_dict, grad_stats, param_stats, actions
 
@@ -114,6 +115,8 @@ if __name__ == "__main__":
             ["policy.n_action_steps=8", "policy.num_inference_steps=10", "policy.down_dims=[128, 256, 512]"],
         ),
         ("aloha", "act", ["policy.n_action_steps=10"]),
+        ("dora_aloha_real", "act_real", ["policy.n_action_steps=10"]),
+        ("dora_aloha_real", "act_real_no_state", ["policy.n_action_steps=10"]),
     ]
     for env, policy, extra_overrides in env_policies:
         save_policy_to_safetensors("tests/data/save_policy_to_safetensors", env, policy, extra_overrides)

tests/test_datasets.py

@@ -16,6 +16,7 @@
 import json
 import logging
 from copy import deepcopy
+from itertools import chain
 from pathlib import Path
 
 import einops
@@ -25,26 +26,34 @@ from datasets import Dataset
 from safetensors.torch import load_file
 
 import lerobot
-from lerobot.common.datasets.factory import make_dataset
-from lerobot.common.datasets.lerobot_dataset import (
-    LeRobotDataset,
-)
-from lerobot.common.datasets.push_dataset_to_hub.compute_stats import (
+from lerobot.common.datasets.compute_stats import (
+    aggregate_stats,
     compute_stats,
     get_stats_einops_patterns,
 )
+from lerobot.common.datasets.factory import make_dataset
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, MultiLeRobotDataset
 from lerobot.common.datasets.utils import (
     flatten_dict,
     hf_transform_to_torch,
     load_previous_and_future_frames,
     unflatten_dict,
 )
-from lerobot.common.utils.utils import init_hydra_config
+from lerobot.common.utils.utils import init_hydra_config, seeded_context
 from tests.utils import DEFAULT_CONFIG_PATH, DEVICE
 
 
-@pytest.mark.parametrize("env_name, repo_id, policy_name", lerobot.env_dataset_policy_triplets)
+@pytest.mark.parametrize(
+    "env_name, repo_id, policy_name",
+    lerobot.env_dataset_policy_triplets
+    + [("aloha", ["lerobot/aloha_sim_insertion_human", "lerobot/aloha_sim_transfer_cube_human"], "act")],
+)
 def test_factory(env_name, repo_id, policy_name):
+    """
+    Tests that:
+        - we can create a dataset with the factory.
+        - for a commonly used set of data keys, the data dimensions are correct.
+    """
     cfg = init_hydra_config(
         DEFAULT_CONFIG_PATH,
         overrides=[
@@ -105,6 +114,39 @@ def test_factory(env_name, repo_id, policy_name):
             assert key in item, f"{key}"
 
 
+# TODO(alexander-soare): If you're hunting for savings on testing time, this takes about 5 seconds.
+def test_multilerobotdataset_frames():
+    """Check that all dataset frames are incorporated."""
+    # Note: use the image variants of the dataset to make the test approx 3x faster.
+    # Note: We really do need three repo_ids here as at some point this caught an issue with the chaining
+    # logic that wouldn't be caught with two repo IDs.
+    repo_ids = [
+        "lerobot/aloha_sim_insertion_human_image",
+        "lerobot/aloha_sim_transfer_cube_human_image",
+        "lerobot/aloha_sim_insertion_scripted_image",
+    ]
+    sub_datasets = [LeRobotDataset(repo_id) for repo_id in repo_ids]
+    dataset = MultiLeRobotDataset(repo_ids)
+    assert len(dataset) == sum(len(d) for d in sub_datasets)
+    assert dataset.num_samples == sum(d.num_samples for d in sub_datasets)
+    assert dataset.num_episodes == sum(d.num_episodes for d in sub_datasets)
+
+    # Run through all items of the LeRobotDatasets in parallel with the items of the MultiLerobotDataset and
+    # check they match.
+    expected_dataset_indices = []
+    for i, sub_dataset in enumerate(sub_datasets):
+        expected_dataset_indices.extend([i] * len(sub_dataset))
+
+    for expected_dataset_index, sub_dataset_item, dataset_item in zip(
+        expected_dataset_indices, chain(*sub_datasets), dataset, strict=True
+    ):
+        dataset_index = dataset_item.pop("dataset_index")
+        assert dataset_index == expected_dataset_index
+        assert sub_dataset_item.keys() == dataset_item.keys()
+        for k in sub_dataset_item:
+            assert torch.equal(sub_dataset_item[k], dataset_item[k])
+
+
 def test_compute_stats_on_xarm():
     """Check that the statistics are computed correctly according to the stats_patterns property.
 
@@ -315,3 +357,31 @@ def test_backward_compatibility(repo_id):
     # i = dataset.episode_data_index["to"][-1].item()
     # load_and_compare(i - 2)
     # load_and_compare(i - 1)
+
+
+def test_aggregate_stats():
+    """Makes 3 basic datasets and checks that aggregate stats are computed correctly."""
+    with seeded_context(0):
+        data_a = torch.rand(30, dtype=torch.float32)
+        data_b = torch.rand(20, dtype=torch.float32)
+        data_c = torch.rand(20, dtype=torch.float32)
+
+    hf_dataset_1 = Dataset.from_dict(
+        {"a": data_a[:10], "b": data_b[:10], "c": data_c[:10], "index": torch.arange(10)}
+    )
+    hf_dataset_1.set_transform(hf_transform_to_torch)
+    hf_dataset_2 = Dataset.from_dict({"a": data_a[10:20], "b": data_b[10:], "index": torch.arange(10)})
+    hf_dataset_2.set_transform(hf_transform_to_torch)
+    hf_dataset_3 = Dataset.from_dict({"a": data_a[20:], "c": data_c[10:], "index": torch.arange(10)})
+    hf_dataset_3.set_transform(hf_transform_to_torch)
+    dataset_1 = LeRobotDataset.from_preloaded("d1", hf_dataset=hf_dataset_1)
+    dataset_1.stats = compute_stats(dataset_1, batch_size=len(hf_dataset_1), num_workers=0)
+    dataset_2 = LeRobotDataset.from_preloaded("d2", hf_dataset=hf_dataset_2)
+    dataset_2.stats = compute_stats(dataset_2, batch_size=len(hf_dataset_2), num_workers=0)
+    dataset_3 = LeRobotDataset.from_preloaded("d3", hf_dataset=hf_dataset_3)
+    dataset_3.stats = compute_stats(dataset_3, batch_size=len(hf_dataset_3), num_workers=0)
+    stats = aggregate_stats([dataset_1, dataset_2, dataset_3])
+    for data_key, data in zip(["a", "b", "c"], [data_a, data_b, data_c], strict=True):
+        for agg_fn in ["mean", "min", "max"]:
+            assert torch.allclose(stats[data_key][agg_fn], einops.reduce(data, "n -> 1", agg_fn))
+        assert torch.allclose(stats[data_key]["std"], torch.std(data, correction=0))

tests/test_policies.py

@@ -30,7 +30,7 @@ from lerobot.common.policies.factory import get_policy_and_config_classes, make_
 from lerobot.common.policies.normalize import Normalize, Unnormalize
 from lerobot.common.policies.policy_protocol import Policy
 from lerobot.common.utils.utils import init_hydra_config
-from tests.scripts.save_policy_to_safetensor import get_policy_stats
+from tests.scripts.save_policy_to_safetensors import get_policy_stats
 from tests.utils import DEFAULT_CONFIG_PATH, DEVICE, require_cpu, require_env, require_x86_64_kernel
 
 
@@ -72,6 +72,8 @@ def test_get_policy_and_config_classes(policy_name: str):
         ),
         # Note: these parameters also need custom logic in the test function for overriding the Hydra config.
         ("pusht", "act", ["env.task=PushT-v0", "dataset_repo_id=lerobot/pusht"]),
+        ("dora_aloha_real", "act_real", []),
+        ("dora_aloha_real", "act_real_no_state", []),
     ],
 )
 @require_env
@@ -84,6 +86,9 @@ def test_policy(env_name, policy_name, extra_overrides):
         - Updating the policy.
         - Using the policy to select actions at inference time.
         - Test the action can be applied to the policy
+
+    Note: We test various combinations of policy and dataset. The combinations are by no means exhaustive,
+          and for now we add tests as we see fit.
     """
     cfg = init_hydra_config(
         DEFAULT_CONFIG_PATH,
@@ -135,7 +140,7 @@ def test_policy(env_name, policy_name, extra_overrides):
 
     dataloader = torch.utils.data.DataLoader(
         dataset,
-        num_workers=4,
+        num_workers=0,
         batch_size=2,
         shuffle=True,
         pin_memory=DEVICE != "cpu",
@@ -291,6 +296,8 @@ def test_normalize(insert_temporal_dim):
             ["policy.n_action_steps=8", "policy.num_inference_steps=10", "policy.down_dims=[128, 256, 512]"],
         ),
         ("aloha", "act", ["policy.n_action_steps=10"]),
+        ("dora_aloha_real", "act_real", ["policy.n_action_steps=10"]),
+        ("dora_aloha_real", "act_real_no_state", ["policy.n_action_steps=10"]),
     ],
 )
 # As artifacts have been generated on an x86_64 kernel, this test won't

tests/test_sampler.py

@@ -0,0 +1,90 @@
+#!/usr/bin/env python
+
+# Copyright 2024 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.
+from datasets import Dataset
+
+from lerobot.common.datasets.sampler import EpisodeAwareSampler
+from lerobot.common.datasets.utils import (
+    calculate_episode_data_index,
+    hf_transform_to_torch,
+)
+
+
+def test_drop_n_first_frames():
+    dataset = Dataset.from_dict(
+        {
+            "timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
+            "index": [0, 1, 2, 3, 4, 5],
+            "episode_index": [0, 0, 1, 2, 2, 2],
+        },
+    )
+    dataset.set_transform(hf_transform_to_torch)
+    episode_data_index = calculate_episode_data_index(dataset)
+    sampler = EpisodeAwareSampler(episode_data_index, drop_n_first_frames=1)
+    assert sampler.indices == [1, 4, 5]
+    assert len(sampler) == 3
+    assert list(sampler) == [1, 4, 5]
+
+
+def test_drop_n_last_frames():
+    dataset = Dataset.from_dict(
+        {
+            "timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
+            "index": [0, 1, 2, 3, 4, 5],
+            "episode_index": [0, 0, 1, 2, 2, 2],
+        },
+    )
+    dataset.set_transform(hf_transform_to_torch)
+    episode_data_index = calculate_episode_data_index(dataset)
+    sampler = EpisodeAwareSampler(episode_data_index, drop_n_last_frames=1)
+    assert sampler.indices == [0, 3, 4]
+    assert len(sampler) == 3
+    assert list(sampler) == [0, 3, 4]
+
+
+def test_episode_indices_to_use():
+    dataset = Dataset.from_dict(
+        {
+            "timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
+            "index": [0, 1, 2, 3, 4, 5],
+            "episode_index": [0, 0, 1, 2, 2, 2],
+        },
+    )
+    dataset.set_transform(hf_transform_to_torch)
+    episode_data_index = calculate_episode_data_index(dataset)
+    sampler = EpisodeAwareSampler(episode_data_index, episode_indices_to_use=[0, 2])
+    assert sampler.indices == [0, 1, 3, 4, 5]
+    assert len(sampler) == 5
+    assert list(sampler) == [0, 1, 3, 4, 5]
+
+
+def test_shuffle():
+    dataset = Dataset.from_dict(
+        {
+            "timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
+            "index": [0, 1, 2, 3, 4, 5],
+            "episode_index": [0, 0, 1, 2, 2, 2],
+        },
+    )
+    dataset.set_transform(hf_transform_to_torch)
+    episode_data_index = calculate_episode_data_index(dataset)
+    sampler = EpisodeAwareSampler(episode_data_index, shuffle=False)
+    assert sampler.indices == [0, 1, 2, 3, 4, 5]
+    assert len(sampler) == 6
+    assert list(sampler) == [0, 1, 2, 3, 4, 5]
+    sampler = EpisodeAwareSampler(episode_data_index, shuffle=True)
+    assert sampler.indices == [0, 1, 2, 3, 4, 5]
+    assert len(sampler) == 6
+    assert set(sampler) == {0, 1, 2, 3, 4, 5}

tests/test_visualize_dataset.py

@@ -25,9 +25,8 @@ from lerobot.scripts.visualize_dataset import visualize_dataset
 def test_visualize_dataset(tmpdir, repo_id):
     rrd_path = visualize_dataset(
         repo_id,
-        episode_index=0,
-        batch_size=32,
-        save=True,
+        episode_indices=[0],
         output_dir=tmpdir,
+        serve=False,
     )
     assert rrd_path.exists()