Add offline inference to visualize_dataset

small adjustment

add rerun

hot fix

WIP

lerobot/common/datasets/push_dataset_to_hub/aloha_dora_format.py

@@ -0,0 +1,215 @@
+#!/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 from dora-record
+"""
+
+import logging
+import re
+from pathlib import Path
+
+import pandas as pd
+import torch
+from datasets import Dataset, Features, Image, Sequence, Value
+
+from lerobot.common.datasets.utils import (
+    hf_transform_to_torch,
+)
+from lerobot.common.datasets.video_utils import VideoFrame
+from lerobot.common.utils.utils import init_logging
+
+
+def check_format(raw_dir) -> bool:
+    assert raw_dir.exists()
+
+    leader_file = list(raw_dir.glob("*.parquet"))
+    if len(leader_file) == 0:
+        raise ValueError(f"Missing parquet files in '{raw_dir}'")
+    return True
+
+
+def load_from_raw(raw_dir: Path, out_dir: Path, fps: int):
+    # Load data stream that will be used as reference for the timestamps synchronization
+    reference_key = "observation.images.cam_right_wrist"
+    reference_df = pd.read_parquet(raw_dir / f"{reference_key}.parquet")
+    reference_df = reference_df[["timestamp_utc", reference_key]]
+
+    # Merge all data stream using nearest backward strategy
+    df = reference_df
+    for path in raw_dir.glob("*.parquet"):
+        key = path.stem  # action or observation.state or ...
+        if key == reference_key or "failed_episode_index" in key:
+            continue
+        modality_df = pd.read_parquet(path)
+        modality_df = modality_df[["timestamp_utc", key]]
+        df = pd.merge_asof(
+            df,
+            modality_df,
+            on="timestamp_utc",
+            direction="nearest",
+            tolerance=pd.Timedelta(f"{1/fps} seconds"),
+        )
+
+    # Remove rows with episode_index -1 which indicates data that correspond to in-between episodes
+    df = df[df["episode_index"] != -1]
+
+    image_keys = [key for key in df if "observation.images." in key]
+
+    def get_episode_index(row):
+        episode_index_per_cam = {}
+        for key in image_keys:
+            path = row[key][0]["path"]
+            match = re.search(r"_(\d{6}).mp4", path)
+            if not match:
+                raise ValueError(path)
+            episode_index = int(match.group(1))
+            episode_index_per_cam[key] = episode_index
+        assert (
+            len(set(episode_index_per_cam.values())) == 1
+        ), f"All cameras are expected to belong to the same episode, but getting {episode_index_per_cam}"
+        return episode_index
+
+    df["episode_index"] = df.apply(get_episode_index, axis=1)
+
+    # dora only use arrays, so single values are encapsulated into a list
+    df["frame_index"] = df.groupby("episode_index").cumcount()
+    df = df.reset_index()
+    df["index"] = df.index
+
+    # set 'next.done' to True for the last frame of each episode
+    df["next.done"] = False
+    df.loc[df.groupby("episode_index").tail(1).index, "next.done"] = True
+
+    df["timestamp"] = df["timestamp_utc"].map(lambda x: x.timestamp())
+    # each episode starts with timestamp 0 to match the ones from the video
+    df["timestamp"] = df.groupby("episode_index")["timestamp"].transform(lambda x: x - x.iloc[0])
+
+    del df["timestamp_utc"]
+
+    # sanity check
+    has_nan = df.isna().any().any()
+    assert not has_nan
+
+    # sanity check episode indices go from 0 to n-1
+    ep_ids = [ep_idx for ep_idx, _ in df.groupby("episode_index")]
+    expected_ep_ids = list(range(df["episode_index"].max() + 1))
+    assert ep_ids == expected_ep_ids, f"Episodes indices go from {ep_ids} instead of {expected_ep_ids}"
+
+    # Create symlink to raw videos directory (that needs to be absolute not relative)
+    out_dir.mkdir(parents=True, exist_ok=True)
+    videos_dir = out_dir / "videos"
+    videos_dir.symlink_to((raw_dir / "videos").absolute())
+
+    # sanity check the video paths are well formated
+    for key in df:
+        if "observation.images." not in key:
+            continue
+        for ep_idx in ep_ids:
+            video_path = videos_dir / f"{key}_episode_{ep_idx:06d}.mp4"
+            assert video_path.exists(), f"Video file not found in {video_path}"
+
+    data_dict = {}
+    for key in df:
+        # is video frame
+        if "observation.images." in key:
+            # we need `[0] because dora only use arrays, so single values are encapsulated into a list.
+            # it is the case for video_frame dictionary = [{"path": ..., "timestamp": ...}]
+            data_dict[key] = [video_frame[0] for video_frame in df[key].values]
+
+            # sanity check the video path is well formated
+            video_path = videos_dir.parent / data_dict[key][0]["path"]
+            assert video_path.exists(), f"Video file not found in {video_path}"
+        # is number
+        elif df[key].iloc[0].ndim == 0 or df[key].iloc[0].shape[0] == 1:
+            data_dict[key] = torch.from_numpy(df[key].values)
+        # is vector
+        elif df[key].iloc[0].shape[0] > 1:
+            data_dict[key] = torch.stack([torch.from_numpy(x.copy()) for x in df[key].values])
+        else:
+            raise ValueError(key)
+
+    # Get the episode index containing for each unique episode index
+    first_ep_index_df = df.groupby("episode_index").agg(start_index=("index", "first")).reset_index()
+    from_ = first_ep_index_df["start_index"].tolist()
+    to_ = from_[1:] + [len(df)]
+    episode_data_index = {
+        "from": from_,
+        "to": to_,
+    }
+
+    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):
+    init_logging()
+
+    if debug:
+        logging.warning("debug=True not implemented. Falling back to debug=False.")
+
+    # sanity check
+    check_format(raw_dir)
+
+    if fps is None:
+        fps = 30
+    else:
+        raise NotImplementedError()
+
+    if not video:
+        raise NotImplementedError()
+
+    data_df, episode_data_index = load_from_raw(raw_dir, out_dir, fps)
+    hf_dataset = to_hf_dataset(data_df, video)
+
+    info = {
+        "fps": fps,
+        "video": video,
+    }
+    return hf_dataset, episode_data_index, info

lerobot/common/envs/factory.py

@@ -27,14 +27,6 @@ def make_env(cfg: DictConfig, n_envs: int | None = None) -> gym.vector.VectorEnv
     if n_envs is not None and n_envs < 1:
         raise ValueError("`n_envs must be at least 1")
 
-    kwargs = {
-        "obs_type": "pixels_agent_pos",
-        "render_mode": "rgb_array",
-        "max_episode_steps": cfg.env.episode_length,
-        "visualization_width": 384,
-        "visualization_height": 384,
-    }
-
     package_name = f"gym_{cfg.env.name}"
 
     try:
@@ -46,12 +38,16 @@ def make_env(cfg: DictConfig, n_envs: int | None = None) -> gym.vector.VectorEnv
         raise e
 
     gym_handle = f"{package_name}/{cfg.env.task}"
+    gym_kwgs = cfg.env.get("gym", {})
+
+    if cfg.env.get("episode_length"):
+        gym_kwgs["max_episode_steps"] = cfg.env.episode_length
 
     # batched version of the env that returns an observation of shape (b, c)
     env_cls = gym.vector.AsyncVectorEnv if cfg.eval.use_async_envs else gym.vector.SyncVectorEnv
     env = env_cls(
         [
-            lambda: gym.make(gym_handle, disable_env_checker=True, **kwargs)
+            lambda: gym.make(gym_handle, disable_env_checker=True, **gym_kwgs)
             for _ in range(n_envs if n_envs is not None else cfg.eval.batch_size)
         ]
     )

lerobot/common/logger.py

@@ -131,9 +131,6 @@ 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 +25,14 @@ 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:
+        - "observation.state" is required as an input key.
+        - At least one key starting with "observation.image is required as an input.
+        - If there are multiple keys beginning with "observation.image" they are treated as multiple camera
+          views.
+          Right now we only support all images having the same shape.
+        - "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 +41,15 @@ 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
@@ -151,7 +159,3 @@ class ACTConfig:
                 f"The chunk size is the upper bound for the number of action steps per model invocation. Got "
                 f"{self.n_action_steps} for `n_action_steps` and {self.chunk_size} for `chunk_size`."
             )
-        if self.n_obs_steps != 1:
-            raise ValueError(
-                f"Multiple observation steps not handled yet. Got `nobs_steps={self.n_obs_steps}`"
-            )

lerobot/common/policies/act/modeling_act.py

@@ -36,6 +36,9 @@ from torchvision.ops.misc import FrozenBatchNorm2d
 
 from lerobot.common.policies.act.configuration_act import ACTConfig
 from lerobot.common.policies.normalize import Normalize, Unnormalize
+from lerobot.common.policies.utils import (
+    populate_queues,
+)
 
 
 class ACTPolicy(nn.Module, PyTorchModelHubMixin):
@@ -73,6 +76,9 @@ class ACTPolicy(nn.Module, PyTorchModelHubMixin):
             config.output_shapes, config.output_normalization_modes, dataset_stats
         )
 
+        # queues are populated during rollout of the policy, they contain the n latest observations and actions
+        self._queues = None
+
         self.model = ACT(config)
 
         self.expected_image_keys = [k for k in config.input_shapes if k.startswith("observation.image")]
@@ -84,7 +90,11 @@ class ACTPolicy(nn.Module, PyTorchModelHubMixin):
         if self.config.temporal_ensemble_momentum is not None:
             self._ensembled_actions = None
         else:
-            self._action_queue = deque([], maxlen=self.config.n_action_steps)
+            self._queues = {
+                "observation.images": deque(maxlen=self.config.n_obs_steps),
+                "observation.state": deque(maxlen=self.config.n_obs_steps),
+                "action": deque(maxlen=self.config.n_action_steps),
+            }
 
     @torch.no_grad
     def select_action(self, batch: dict[str, Tensor]) -> Tensor:
@@ -98,10 +108,15 @@ class ACTPolicy(nn.Module, PyTorchModelHubMixin):
 
         batch = self.normalize_inputs(batch)
         batch["observation.images"] = torch.stack([batch[k] for k in self.expected_image_keys], dim=-4)
+        # Note: It's important that this happens after stacking the images into a single key.
+        self._queues = populate_queues(self._queues, batch)
 
         # If we are doing temporal ensembling, keep track of the exponential moving average (EMA), and return
         # the first action.
         if self.config.temporal_ensemble_momentum is not None:
+            # stack n latest observations from the queue
+            batch = {k: torch.stack(list(self._queues[k]), dim=1) for k in batch if k in self._queues}
+
             actions = self.model(batch)[0]  # (batch_size, chunk_size, action_dim)
             actions = self.unnormalize_outputs({"action": actions})["action"]
             if self._ensembled_actions is None:
@@ -121,7 +136,10 @@ class ACTPolicy(nn.Module, PyTorchModelHubMixin):
 
         # Action queue logic for n_action_steps > 1. When the action_queue is depleted, populate it by
         # querying the policy.
-        if len(self._action_queue) == 0:
+        if len(self._queues["action"]) == 0:
+            # stack n latest observations from the queue
+            batch = {k: torch.stack(list(self._queues[k]), dim=1) for k in batch if k in self._queues}
+
             actions = self.model(batch)[0][:, : self.config.n_action_steps]
 
             # TODO(rcadene): make _forward return output dictionary?
@@ -129,8 +147,8 @@ class ACTPolicy(nn.Module, PyTorchModelHubMixin):
 
             # `self.model.forward` returns a (batch_size, n_action_steps, action_dim) tensor, but the queue
             # effectively has shape (n_action_steps, batch_size, *), hence the transpose.
-            self._action_queue.extend(actions.transpose(0, 1))
-        return self._action_queue.popleft()
+            self._queues["action"].extend(actions.transpose(0, 1))
+        return self._queues["action"].popleft()
 
     def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
         """Run the batch through the model and compute the loss for training or validation."""
@@ -139,25 +157,27 @@ 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["actions"] = self.unnormalize_outputs({"action": actions_hat})["action"]
+        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["kld_loss"] = kld_loss
+            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
+        return out_dict
 
 
 class ACT(nn.Module):
@@ -200,25 +220,28 @@ class ACT(nn.Module):
         self.config = config
         # BERT style VAE encoder with input [cls, *joint_space_configuration, *action_sequence].
         # The cls token forms parameters of the latent's distribution (like this [*means, *log_variances]).
+        self.has_state = "observation.state" in config.input_shapes
+        self.latent_dim = config.latent_dim
         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.has_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
             # dimension.
+            num_input_token_encoder = 1 + 1 + config.chunk_size if self.has_state else 1 + config.chunk_size
             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.
@@ -232,21 +255,30 @@ class ACT(nn.Module):
         # Note: The forward method of this returns a dict: {"feature_map": output}.
         self.backbone = IntermediateLayerGetter(backbone_model, return_layers={"layer4": "feature_map"})
 
+        if self.config.n_obs_steps > 1:
+            self.factorized_conv3d = FactorizedConv3d(
+                backbone_model.fc.in_features,
+                backbone_model.fc.in_features,
+                backbone_model.fc.in_features,
+            )
+
         # Transformer (acts as VAE decoder when training with the variational objective).
         self.encoder = ACTEncoder(config)
         self.decoder = ACTDecoder(config)
 
         # 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
-        )
+        if self.has_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(self.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.has_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.
@@ -285,7 +317,7 @@ 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 +325,16 @@ 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.has_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.has_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.
@@ -317,6 +354,7 @@ class ACT(nn.Module):
         else:
             # When not using the VAE encoder, we set the latent to be all zeros.
             mu = log_sigma_x2 = None
+            # TODO(rcadene, alexander-soare): remove call to `.to` to speedup forward ; precompute and use buffer
             latent_sample = torch.zeros([batch_size, self.latent_dim], dtype=torch.float32).to(
                 batch["observation.state"].device
             )
@@ -326,8 +364,25 @@ class ACT(nn.Module):
         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"]
+            if self.config.n_obs_steps > 1:
+                assert images.ndim == 6
+                assert images.shape[1] == self.config.n_obs_steps
+                cam_images = images[:, :, cam_index]
+                b, t, c_in, h_in, w_in = cam_images.shape
+                cam_images = cam_images.reshape(b * t, c_in, h_in, w_in)
+                cam_features = self.backbone(cam_images)["feature_map"]
+                bt_, c_out, h_out, w_out = cam_features.shape
+                cam_features = cam_features.view(b, t, c_out, h_out, w_out)
+                cam_features = cam_features.permute(0, 2, 1, 3, 4).contiguous()
+                cam_features = self.factorized_conv3d(cam_features)
+                cam_features = cam_features.mean(2)
+            else:
+                assert images.ndim == 5
+                cam_image = images[:, cam_index]
+                cam_features = self.backbone(cam_image)["feature_map"]
+            # TODO(rcadene, alexander-soare): remove call to `.to` to speedup forward ; precompute and use buffer
             cam_pos_embed = self.encoder_cam_feat_pos_embed(cam_features).to(dtype=cam_features.dtype)
             cam_features = self.encoder_img_feat_input_proj(cam_features)  # (B, C, h, w)
             all_cam_features.append(cam_features)
@@ -337,13 +392,15 @@ 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.has_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.has_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"),
             ]
         )
@@ -357,6 +414,7 @@ class ACT(nn.Module):
 
         # Forward pass through the transformer modules.
         encoder_out = self.encoder(encoder_in, pos_embed=pos_embed)
+        # TODO(rcadene, alexander-soare): remove call to `device` ; precompute and use buffer
         decoder_in = torch.zeros(
             (self.config.chunk_size, batch_size, self.config.dim_model),
             dtype=pos_embed.dtype,
@@ -606,3 +664,24 @@ def get_activation_fn(activation: str) -> Callable:
     if activation == "glu":
         return F.glu
     raise RuntimeError(f"activation should be relu/gelu/glu, not {activation}.")
+
+
+class FactorizedConv3d(nn.Module):
+    def __init__(self, in_channels, hidden_dim, out_channels):
+        super().__init__()
+        self.in_channels = in_channels
+        self.hidden_dim = hidden_dim
+        self.out_channels = out_channels
+        self.spatial_conv = nn.Conv3d(in_channels, hidden_dim, kernel_size=(1, 3, 3), padding=(0, 1, 1))
+        self.temporal_conv = nn.Conv3d(hidden_dim, out_channels, kernel_size=(3, 1, 1), padding=(1, 0, 0))
+
+    def forward(self, x):
+        assert x.ndim == 5, f"Expected shape is b,t,c,h,w but {x.shape} given."
+        assert (
+            x.shape[1] == self.in_channels
+        ), f"Expected number channels as input is {self.in_channels}, but {x.shape[2]} given."
+        x = self.spatial_conv(x)
+        x = F.relu(x, inplace=True)
+        x = self.temporal_conv(x)
+        x = F.relu(x, inplace=True)
+        return x

lerobot/common/policies/diffusion/configuration_diffusion.py

@@ -26,21 +26,29 @@ 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.
+        - At least one key starting with "observation.image is required as an input.
+        - If there are multiple keys beginning with "observation.image" they are treated as multiple camera
+          views.
+          Right now we only support all images having the same shape.
+        - "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
@@ -148,22 +156,26 @@ class DiffusionConfig:
             raise ValueError(
                 f"`vision_backbone` must be one of the ResNet variants. Got {self.vision_backbone}."
             )
-        # There should only be one image key.
         image_keys = {k for k in self.input_shapes if k.startswith("observation.image")}
-        if len(image_keys) != 1:
-            raise ValueError(
-                f"{self.__class__.__name__} only handles one image for now. Got image keys {image_keys}."
-            )
-        image_key = next(iter(image_keys))
-        if (
-            self.crop_shape[0] > self.input_shapes[image_key][1]
-            or self.crop_shape[1] > self.input_shapes[image_key][2]
-        ):
-            raise ValueError(
-                f"`crop_shape` should fit within `input_shapes[{image_key}]`. Got {self.crop_shape} "
-                f"for `crop_shape` and {self.input_shapes[image_key]} for "
-                "`input_shapes[{image_key}]`."
-            )
+        if self.crop_shape is not None:
+            for image_key in image_keys:
+                if (
+                    self.crop_shape[0] > self.input_shapes[image_key][1]
+                    or self.crop_shape[1] > self.input_shapes[image_key][2]
+                ):
+                    raise ValueError(
+                        f"`crop_shape` should fit within `input_shapes[{image_key}]`. Got {self.crop_shape} "
+                        f"for `crop_shape` and {self.input_shapes[image_key]} for "
+                        "`input_shapes[{image_key}]`."
+                    )
+        # Check that all input images have the same shape.
+        first_image_key = next(iter(image_keys))
+        for image_key in image_keys:
+            if self.input_shapes[image_key] != self.input_shapes[first_image_key]:
+                raise ValueError(
+                    f"`input_shapes[{image_key}]` does not match `input_shapes[{first_image_key}]`, but we "
+                    "expect all image shapes to match."
+                )
         supported_prediction_types = ["epsilon", "sample"]
         if self.prediction_type not in supported_prediction_types:
             raise ValueError(

lerobot/common/policies/diffusion/modeling_diffusion.py

@@ -18,7 +18,6 @@
 
 TODO(alexander-soare):
   - Remove reliance on diffusers for DDPMScheduler and LR scheduler.
-  - Make compatible with multiple image keys.
 """
 
 import math
@@ -83,20 +82,14 @@ class DiffusionPolicy(nn.Module, PyTorchModelHubMixin):
 
         self.diffusion = DiffusionModel(config)
 
-        image_keys = [k for k in config.input_shapes if k.startswith("observation.image")]
-        # Note: This check is covered in the post-init of the config but have a sanity check just in case.
-        if len(image_keys) != 1:
-            raise NotImplementedError(
-                f"{self.__class__.__name__} only handles one image for now. Got image keys {image_keys}."
-            )
-        self.input_image_key = image_keys[0]
+        self.expected_image_keys = [k for k in config.input_shapes if k.startswith("observation.image")]
 
         self.reset()
 
     def reset(self):
         """Clear observation and action queues. Should be called on `env.reset()`"""
         self._queues = {
-            "observation.image": deque(maxlen=self.config.n_obs_steps),
+            "observation.images": deque(maxlen=self.config.n_obs_steps),
             "observation.state": deque(maxlen=self.config.n_obs_steps),
             "action": deque(maxlen=self.config.n_action_steps),
         }
@@ -124,8 +117,8 @@ class DiffusionPolicy(nn.Module, PyTorchModelHubMixin):
         actually measured from the first observation which (if `n_obs_steps` > 1) happened in the past.
         """
         batch = self.normalize_inputs(batch)
-        batch["observation.image"] = batch[self.input_image_key]
-
+        batch["observation.images"] = torch.stack([batch[k] for k in self.expected_image_keys], dim=-4)
+        # Note: It's important that this happens after stacking the images into a single key.
         self._queues = populate_queues(self._queues, batch)
 
         if len(self._queues["action"]) == 0:
@@ -144,7 +137,7 @@ class DiffusionPolicy(nn.Module, PyTorchModelHubMixin):
     def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
         """Run the batch through the model and compute the loss for training or validation."""
         batch = self.normalize_inputs(batch)
-        batch["observation.image"] = batch[self.input_image_key]
+        batch["observation.images"] = torch.stack([batch[k] for k in self.expected_image_keys], dim=-4)
         batch = self.normalize_targets(batch)
         loss = self.diffusion.compute_loss(batch)
         return {"loss": loss}
@@ -169,9 +162,10 @@ class DiffusionModel(nn.Module):
         self.config = config
 
         self.rgb_encoder = DiffusionRgbEncoder(config)
+        num_images = len([k for k in config.input_shapes if k.startswith("observation.image")])
         self.unet = DiffusionConditionalUnet1d(
             config,
-            global_cond_dim=(config.output_shapes["action"][0] + self.rgb_encoder.feature_dim)
+            global_cond_dim=(config.output_shapes["action"][0] + self.rgb_encoder.feature_dim * num_images)
             * config.n_obs_steps,
         )
 
@@ -220,23 +214,34 @@ class DiffusionModel(nn.Module):
 
         return sample
 
+    def _prepare_global_conditioning(self, batch: dict[str, Tensor]) -> Tensor:
+        """Encode image features and concatenate them all together along with the state vector."""
+        batch_size, n_obs_steps = batch["observation.state"].shape[:2]
+        # Extract image feature (first combine batch, sequence, and camera index dims).
+        img_features = self.rgb_encoder(
+            einops.rearrange(batch["observation.images"], "b s n ... -> (b s n) ...")
+        )
+        # Separate batch dim and sequence dim back out. The camera index dim gets absorbed into the feature
+        # dim (effectively concatenating the camera features).
+        img_features = einops.rearrange(
+            img_features, "(b s n) ... -> b s (n ...)", b=batch_size, s=n_obs_steps
+        )
+        # Concatenate state and image features then flatten to (B, global_cond_dim).
+        return torch.cat([batch["observation.state"], img_features], dim=-1).flatten(start_dim=1)
+
     def generate_actions(self, batch: dict[str, Tensor]) -> Tensor:
         """
         This function expects `batch` to have:
         {
             "observation.state": (B, n_obs_steps, state_dim)
-            "observation.image": (B, n_obs_steps, C, H, W)
+            "observation.images": (B, n_obs_steps, num_cameras, C, H, W)
         }
         """
         batch_size, n_obs_steps = batch["observation.state"].shape[:2]
         assert n_obs_steps == self.config.n_obs_steps
 
-        # Extract image feature (first combine batch and sequence dims).
-        img_features = self.rgb_encoder(einops.rearrange(batch["observation.image"], "b n ... -> (b n) ..."))
-        # Separate batch and sequence dims.
-        img_features = einops.rearrange(img_features, "(b n) ... -> b n ...", b=batch_size)
-        # Concatenate state and image features then flatten to (B, global_cond_dim).
-        global_cond = torch.cat([batch["observation.state"], img_features], dim=-1).flatten(start_dim=1)
+        # Encode image features and concatenate them all together along with the state vector.
+        global_cond = self._prepare_global_conditioning(batch)  # (B, global_cond_dim)
 
         # run sampling
         sample = self.conditional_sample(batch_size, global_cond=global_cond)
@@ -255,28 +260,23 @@ class DiffusionModel(nn.Module):
         This function expects `batch` to have (at least):
         {
             "observation.state": (B, n_obs_steps, state_dim)
-            "observation.image": (B, n_obs_steps, C, H, W)
+            "observation.images": (B, n_obs_steps, num_cameras, C, H, W)
             "action": (B, horizon, action_dim)
             "action_is_pad": (B, horizon)
         }
         """
         # Input validation.
-        assert set(batch).issuperset({"observation.state", "observation.image", "action", "action_is_pad"})
-        batch_size, n_obs_steps = batch["observation.state"].shape[:2]
+        assert set(batch).issuperset({"observation.state", "observation.images", "action", "action_is_pad"})
+        n_obs_steps = batch["observation.state"].shape[1]
         horizon = batch["action"].shape[1]
         assert horizon == self.config.horizon
         assert n_obs_steps == self.config.n_obs_steps
 
-        # Extract image feature (first combine batch and sequence dims).
-        img_features = self.rgb_encoder(einops.rearrange(batch["observation.image"], "b n ... -> (b n) ..."))
-        # Separate batch and sequence dims.
-        img_features = einops.rearrange(img_features, "(b n) ... -> b n ...", b=batch_size)
-        # Concatenate state and image features then flatten to (B, global_cond_dim).
-        global_cond = torch.cat([batch["observation.state"], img_features], dim=-1).flatten(start_dim=1)
-
-        trajectory = batch["action"]
+        # Encode image features and concatenate them all together along with the state vector.
+        global_cond = self._prepare_global_conditioning(batch)  # (B, global_cond_dim)
 
         # Forward diffusion.
+        trajectory = batch["action"]
         # Sample noise to add to the trajectory.
         eps = torch.randn(trajectory.shape, device=trajectory.device)
         # Sample a random noising timestep for each item in the batch.
@@ -304,7 +304,12 @@ class DiffusionModel(nn.Module):
         loss = F.mse_loss(pred, target, reduction="none")
 
         # Mask loss wherever the action is padded with copies (edges of the dataset trajectory).
-        if self.config.do_mask_loss_for_padding and "action_is_pad" in batch:
+        if self.config.do_mask_loss_for_padding:
+            if "action_is_pad" not in batch:
+                raise ValueError(
+                    "You need to provide 'action_is_pad' in the batch when "
+                    f"{self.config.do_mask_loss_for_padding=}."
+                )
             in_episode_bound = ~batch["action_is_pad"]
             loss = loss * in_episode_bound.unsqueeze(-1)
 
@@ -425,9 +430,12 @@ class DiffusionRgbEncoder(nn.Module):
         # The dummy input should take the number of image channels from `config.input_shapes` and it should
         # use the height and width from `config.crop_shape`.
         image_keys = [k for k in config.input_shapes if k.startswith("observation.image")]
-        assert len(image_keys) == 1
+        # Note: we have a check in the config class to make sure all images have the same shape.
         image_key = image_keys[0]
-        dummy_input = torch.zeros(size=(1, config.input_shapes[image_key][0], *config.crop_shape))
+        if config.crop_shape is None:
+            dummy_input = torch.zeros(size=(1, *config.input_shapes[image_key]))
+        else:
+            dummy_input = torch.zeros(size=(1, config.input_shapes[image_key][0], *config.crop_shape))
         with torch.inference_mode():
             dummy_feature_map = self.backbone(dummy_input)
         feature_map_shape = tuple(dummy_feature_map.shape[1:])

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/configs/default.yaml

@@ -26,6 +26,8 @@ training:
   save_freq: ???
   log_freq: 250
   save_model: true
+  num_workers: 4
+  batch_size: ???
 
 eval:
   n_episodes: 1

lerobot/configs/env/aloha.yaml

@@ -5,10 +5,13 @@ fps: 50
 env:
   name: aloha
   task: AlohaInsertion-v0
-  from_pixels: True
-  pixels_only: False
   image_size: [3, 480, 640]
-  episode_length: 400
-  fps: ${fps}
   state_dim: 14
   action_dim: 14
+  fps: ${fps}
+  episode_length: 400
+  gym:
+    obs_type: pixels_agent_pos
+    render_mode: rgb_array
+    visualization_width: 384
+    visualization_height: 384

lerobot/configs/env/aloha2_real.yaml

@@ -0,0 +1,13 @@
+# @package _global_
+
+fps: 30
+
+env:
+  name: dora
+  task: DoraAloha2-v0
+  state_dim: 14
+  action_dim: 14
+  fps: ${fps}
+  episode_length: 400
+  gym:
+    fps: ${fps}

lerobot/configs/env/pusht.yaml

@@ -5,10 +5,13 @@ fps: 10
 env:
   name: pusht
   task: PushT-v0
-  from_pixels: True
-  pixels_only: False
   image_size: 96
-  episode_length: 300
-  fps: ${fps}
   state_dim: 2
   action_dim: 2
+  fps: ${fps}
+  episode_length: 300
+  gym:
+    obs_type: pixels_agent_pos
+    render_mode: rgb_array
+    visualization_width: 384
+    visualization_height: 384

lerobot/configs/env/xarm.yaml

@@ -5,10 +5,13 @@ fps: 15
 env:
   name: xarm
   task: XarmLift-v0
-  from_pixels: True
-  pixels_only: False
   image_size: 84
-  episode_length: 25
-  fps: ${fps}
   state_dim: 4
   action_dim: 4
+  fps: ${fps}
+  episode_length: 25
+  gym:
+    obs_type: pixels_agent_pos
+    render_mode: rgb_array
+    visualization_width: 384
+    visualization_height: 384

lerobot/configs/policy/act_real.yaml

@@ -0,0 +1,101 @@
+# @package _global_
+
+seed: 1000
+dataset_repo_id: cadene/aloha_v2_static_dora_test
+
+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_model: 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 / ${fps} for i in range(${policy.chunk_size})]"
+
+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/diffusion_real.yaml

@@ -0,0 +1,114 @@
+# @package _global_
+
+# Defaults for training for the PushT dataset as per https://github.com/real-stanford/diffusion_policy.
+# Note: We do not track EMA model weights as we discovered it does not improve the results. See
+#       https://github.com/huggingface/lerobot/pull/134 for more details.
+
+seed: 100000
+dataset_repo_id: lerobot/pusht
+
+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: 200000
+  online_steps: 0
+  eval_freq: -1
+  save_freq: 1000
+  log_freq: 100
+  save_model: true
+
+  batch_size: 64
+  grad_clip_norm: 10
+  lr: 1.0e-4
+  lr_scheduler: cosine
+  lr_warmup_steps: 500
+  adam_betas: [0.95, 0.999]
+  adam_eps: 1.0e-8
+  adam_weight_decay: 1.0e-6
+  online_steps_between_rollouts: 1
+
+  delta_timestamps:
+    observation.images.cam_right_wrist: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1)]"
+    observation.images.cam_left_wrist: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1)]"
+    observation.images.cam_high: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1)]"
+    observation.images.cam_low: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1)]"
+    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} + ${policy.horizon})]"
+
+eval:
+  n_episodes: 50
+  batch_size: 50
+
+policy:
+  name: diffusion
+
+  # Input / output structure.
+  n_obs_steps: 2
+  horizon: 16
+  n_action_steps: 8
+
+  input_shapes:
+    # TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env?
+    observation.images.cam_right_wrist: [3, 480, 640]
+    observation.images.cam_left_wrist: [3, 480, 640]
+    observation.images.cam_high: [3, 480, 640]
+    observation.images.cam_low: [3, 480, 640]
+    observation.state: ["${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 / modeling.
+  # Vision backbone.
+  vision_backbone: resnet18
+  crop_shape: null
+  crop_is_random: False
+  pretrained_backbone_weights: ResNet18_Weights.IMAGENET1K_V1
+  use_group_norm: False
+  spatial_softmax_num_keypoints: 32
+  # Unet.
+  down_dims: [512, 1024, 2048]
+  kernel_size: 5
+  n_groups: 8
+  diffusion_step_embed_dim: 128
+  use_film_scale_modulation: True
+  # Noise scheduler.
+  noise_scheduler_type: DDPM
+  num_train_timesteps: 100
+  beta_schedule: squaredcos_cap_v2
+  beta_start: 0.0001
+  beta_end: 0.02
+  prediction_type: epsilon # epsilon / sample
+  clip_sample: True
+  clip_sample_range: 1.0
+
+  # Inference
+  num_inference_steps: 100
+
+  # Loss computation
+  do_mask_loss_for_padding: false

lerobot/scripts/push_dataset_to_hub.py

@@ -84,10 +84,14 @@ def get_from_raw_to_lerobot_format_fn(raw_format):
         from lerobot.common.datasets.push_dataset_to_hub.umi_zarr_format import from_raw_to_lerobot_format
     elif raw_format == "aloha_hdf5":
         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 == "xarm_pkl":
         from lerobot.common.datasets.push_dataset_to_hub.xarm_pkl_format import from_raw_to_lerobot_format
     else:
-        raise ValueError(raw_format)
+        raise ValueError(
+            f"The selected {raw_format} can't be found. Did you add it to `lerobot/scripts/push_dataset_to_hub.py::get_from_raw_to_lerobot_format_fn`?"
+        )
 
     return from_raw_to_lerobot_format
 

lerobot/scripts/train.py

@@ -93,7 +93,7 @@ def update_policy(policy, batch, optimizer, grad_clip_norm, lr_scheduler=None):
     policy.train()
     output_dict = policy.forward(batch)
     # TODO(rcadene): policy.unnormalize_outputs(out_dict)
-    loss = output_dict["loss"]
+    loss = output_dict["loss"].mean()
     loss.backward()
     grad_norm = torch.nn.utils.clip_grad_norm_(
         policy.parameters(),
@@ -329,8 +329,12 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
     logging.info("make_dataset")
     offline_dataset = make_dataset(cfg)
 
-    logging.info("make_env")
-    eval_env = make_env(cfg)
+    # 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,
+    # using the eval.py instead, with gym_dora environment and dora-rs.
+    if cfg.training.eval_freq > 0:
+        logging.info("make_env")
+        eval_env = make_env(cfg)
 
     logging.info("make_policy")
     policy = make_policy(hydra_cfg=cfg, dataset_stats=offline_dataset.stats)
@@ -356,7 +360,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
 
     # Note: this helper will be used in offline and online training loops.
     def evaluate_and_checkpoint_if_needed(step):
-        if step % cfg.training.eval_freq == 0:
+        if cfg.training.eval_freq > 0 and step % cfg.training.eval_freq == 0:
             logging.info(f"Eval policy at step {step}")
             eval_info = eval_policy(
                 eval_env,
@@ -386,7 +390,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
     # create dataloader for offline training
     dataloader = torch.utils.data.DataLoader(
         offline_dataset,
-        num_workers=4,
+        num_workers=cfg.training.num_workers,
         batch_size=cfg.training.batch_size,
         shuffle=True,
         pin_memory=cfg.device != "cpu",
@@ -417,6 +421,13 @@ 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)
 
@@ -486,9 +497,10 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
             step += 1
             online_step += 1
 
+    logging.info("End of online training")
+
     eval_env.close()
     online_training_env.close()
-    logging.info("End of training")
 
 
 if __name__ == "__main__":

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 1.8.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -404,28 +404,28 @@ files = [
 
 [[package]]
 name = "cmake"
-version = "3.29.2"
+version = "3.29.3"
 description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "cmake-3.29.2-py3-none-macosx_10_10_universal2.macosx_10_10_x86_64.macosx_11_0_arm64.macosx_11_0_universal2.whl", hash = "sha256:1d40c5451d6467b20a0a6015a5a6b6dc86f61b83f71f935740485b259100a34e"},
-    {file = "cmake-3.29.2-py3-none-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:ed3108e646cd65a4e23fa1cbe8123569a29334a3f2a8ce214d871406b161bedb"},
-    {file = "cmake-3.29.2-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:40aafe612b03a9fa140cca4024ba60b74cd92372f3f349d8062cba1f021e5001"},
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@@ -767,6 +767,26 @@ files = [
 [package.dependencies]
 six = ">=1.4.0"
 
+[[package]]
+name = "dora-rs"
+version = "0.3.4"
+description = "`dora` goal is to be a low latency, composable, and distributed data flow."
+optional = true
+python-versions = "*"
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+
 [[package]]
 name = "einops"
 version = "0.8.0"
@@ -956,13 +976,13 @@ tqdm = ["tqdm"]
 
 [[package]]
 name = "gdown"
-version = "5.1.0"
+version = "5.2.0"
 description = "Google Drive Public File/Folder Downloader"
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 python-versions = ">=3.8"
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 [package.dependencies]
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 tqdm = "*"
 
 [package.extras]
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+test = ["build", "mypy", "pytest", "pytest-xdist", "ruff", "twine", "types-requests", "types-setuptools"]
 
 [[package]]
 name = "gitdb"
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 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 = true
+
+[package.dependencies]
+dora-rs = ">=0.3.4"
+gymnasium = ">=0.29.1"
+pyarrow = ">=12.0.0"
+
+[package.source]
+type = "directory"
+url = "gym_dora"
+
 [[package]]
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+version = "0.1.4"
 description = "A gymnasium environment for PushT."
 optional = true
 python-versions = "<4.0,>=3.10"
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 ]
 
 [[package]]
 name = "requests"
-version = "2.31.0"
+version = "2.32.1"
 description = "Python HTTP for Humans."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3192,22 +3218,22 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "rerun-sdk"
-version = "0.15.1"
+version = "0.16.0"
 description = "The Rerun Logging SDK"
 optional = false
 python-versions = "<3.13,>=3.8"
 files = [
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 ]
 
 [package.dependencies]
 attrs = ">=23.1.0"
 numpy = ">=1.23,<2"
-pillow = "*"
+pillow = ">=8.0.0"
 pyarrow = ">=14.0.2"
 typing-extensions = ">=4.5"
 
@@ -3423,13 +3449,13 @@ test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "po
 
 [[package]]
 name = "sentry-sdk"
-version = "2.1.1"
+version = "2.2.1"
 description = "Python client for Sentry (https://sentry.io)"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "sentry_sdk-2.1.1-py2.py3-none-any.whl", hash = "sha256:99aeb78fb76771513bd3b2829d12613130152620768d00cd3e45ac00cb17950f"},
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 ]
 
 [package.dependencies]
@@ -3451,7 +3477,7 @@ django = ["django (>=1.8)"]
 falcon = ["falcon (>=1.4)"]
 fastapi = ["fastapi (>=0.79.0)"]
 flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
-grpcio = ["grpcio (>=1.21.1)"]
+grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
 httpx = ["httpx (>=0.16.0)"]
 huey = ["huey (>=2)"]
 huggingface-hub = ["huggingface-hub (>=0.22)"]
@@ -3573,19 +3599,18 @@ test = ["pytest"]
 
 [[package]]
 name = "setuptools"
-version = "69.5.1"
+version = "70.0.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "setuptools-70.0.0.tar.gz", hash = "sha256:f211a66637b8fa059bb28183da127d4e86396c991a942b028c6650d4319c3fd0"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
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+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
+testing = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
 
 [[package]]
 name = "shapely"
@@ -3921,13 +3946,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "virtualenv"
-version = "20.26.1"
+version = "20.26.2"
 description = "Virtual Python Environment builder"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "virtualenv-20.26.1-py3-none-any.whl", hash = "sha256:7aa9982a728ae5892558bff6a2839c00b9ed145523ece2274fad6f414690ae75"},
-    {file = "virtualenv-20.26.1.tar.gz", hash = "sha256:604bfdceaeece392802e6ae48e69cec49168b9c5f4a44e483963f9242eb0e78b"},
+    {file = "virtualenv-20.26.2-py3-none-any.whl", hash = "sha256:a624db5e94f01ad993d476b9ee5346fdf7b9de43ccaee0e0197012dc838a0e9b"},
+    {file = "virtualenv-20.26.2.tar.gz", hash = "sha256:82bf0f4eebbb78d36ddaee0283d43fe5736b53880b8a8cdcd37390a07ac3741c"},
 ]
 
 [package.dependencies]
@@ -4203,13 +4228,13 @@ multidict = ">=4.0"
 
 [[package]]
 name = "zarr"
-version = "2.18.0"
+version = "2.18.1"
 description = "An implementation of chunked, compressed, N-dimensional arrays for Python"
 optional = false
 python-versions = ">=3.9"
 files = [
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-    {file = "zarr-2.18.0.tar.gz", hash = "sha256:c3b7d2c85b8a42b0ad0ad268a36fb6886ca852098358c125c6b126a417e0a598"},
+    {file = "zarr-2.18.1-py3-none-any.whl", hash = "sha256:a1770d194eec4ec0a41a01295a6f724e1c3471d704d3aca906d3b3a7f8830245"},
+    {file = "zarr-2.18.1.tar.gz", hash = "sha256:28c360ed123e606c425a694a83300227a907cb86a995fc9eef620ecafbe5f92d"},
 ]
 
 [package.dependencies]
@@ -4224,22 +4249,23 @@ jupyter = ["ipytree (>=0.2.2)", "ipywidgets (>=8.0.0)", "notebook"]
 
 [[package]]
 name = "zipp"
-version = "3.18.1"
+version = "3.18.2"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
-    {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
+    {file = "zipp-3.18.2-py3-none-any.whl", hash = "sha256:dce197b859eb796242b0622af1b8beb0a722d52aa2f57133ead08edd5bf5374e"},
+    {file = "zipp-3.18.2.tar.gz", hash = "sha256:6278d9ddbcfb1f1089a88fde84481528b07b0e10474e09dcfe53dad4069fa059"},
 ]
 
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [extras]
 aloha = ["gym-aloha"]
 dev = ["debugpy", "pre-commit"]
+dora = ["gym-dora"]
 pusht = ["gym-pusht"]
 test = ["pytest", "pytest-cov"]
 umi = ["imagecodecs"]
@@ -4248,4 +4274,4 @@ xarm = ["gym-xarm"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "e4834d67df32c8c617c259b0e59bb33ddaccde08fe940d771e74046cbffe3399"
+content-hash = "ea4e8207316a8ec8a4b95d6a89cf488c8733a8e7ab43e5f669c889ee87f3bef3"

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 = { path = "gym_dora", optional = true, develop = 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}
@@ -61,6 +62,7 @@ rerun-sdk = ">=0.15.1"
 
 
 [tool.poetry.extras]
+dora = ["gym-dora"]
 pusht = ["gym-pusht"]
 xarm = ["gym-xarm"]
 aloha = ["gym-aloha"]

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()