fix(pandas->parquet): switching from a pandas to a native parquet approach for aggregating and editing datasets

* chore(docstrings): updating v2.1-v3.0 conversion script docstrings to match the new task label

* chore(task): renamming the default index label in the tasks DataFrame to task

* Revert "chore(docstrings): updating v2.1-v3.0 conversion script docstrings to match the new task label"

This reverts commit f55de3255278f23f18b5d955565f6768d094951d.

* chore(docstrings): updating docstrings to match dataset v3.0 architecture

* chore(format): formatting code

docs/source/async.mdx

@@ -48,7 +48,7 @@ python -m lerobot.async_inference.robot_client \
     --task="dummy" \ # POLICY: The task to run the policy on (`Fold my t-shirt`). Not necessarily defined for all policies, such as `act`
     --policy_type=your_policy_type \ # POLICY: the type of policy to run (smolvla, act, etc)
     --pretrained_name_or_path=user/model \ # POLICY: the model name/path on server to the checkpoint to run (e.g., lerobot/smolvla_base)
-    --policy_device=mps \ # POLICY: the device to run the policy on, on the server
+    --policy_device=mps \ # POLICY: the device to run the policy on, on the server (cuda, mps, xpu, cpu)
     --actions_per_chunk=50 \ # POLICY: the number of actions to output at once
     --chunk_size_threshold=0.5 \ # CLIENT: the threshold for the chunk size before sending a new observation to the server
     --aggregate_fn_name=weighted_average \ # CLIENT: the function to aggregate actions on overlapping portions

docs/source/earthrover_mini_plus.mdx

@@ -170,13 +170,13 @@ Once you can drive the robot well, you can start recording data to train AI mode
 We use Hugging Face to store your data online. First, log in with your token from [Hugging Face settings](https://huggingface.co/settings/tokens):
 
 ```bash
-huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+hf auth login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
 ```
 
 Store your Hugging Face username:
 
 ```bash
-HF_USER=$(huggingface-cli whoami | head -n 1)
+HF_USER=$(hf auth whoami | awk -F': *' 'NR==1 {print $2}')
 echo $HF_USER
 ```
 

docs/source/envhub.mdx

@@ -155,10 +155,10 @@ Upload your repository to Hugging Face:
 pip install huggingface_hub
 
 # Login to Hugging Face
-huggingface-cli login
+hf auth login
 
 # Create a new repository
-huggingface-cli repo create my-custom-env --type space --org my-org
+hf repo create my-org/my-custom-env
 
 # Initialize git and push
 git init

docs/source/il_robots.mdx

@@ -159,7 +159,7 @@ We use the Hugging Face hub features for uploading your dataset. If you haven't
 Add your token to the CLI by running this command:
 
 ```bash
-huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+hf auth login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
 ```
 
 Then store your Hugging Face repository name in a variable:
@@ -327,7 +327,7 @@ You can look for other LeRobot datasets on the hub by searching for `LeRobot` [t
 You can also push your local dataset to the Hub manually, running:
 
 ```bash
-huggingface-cli upload ${HF_USER}/record-test ~/.cache/huggingface/lerobot/{repo-id} --repo-type dataset
+hf upload ${HF_USER}/record-test ~/.cache/huggingface/lerobot/{repo-id} --repo-type dataset
 ```
 
 #### Record function
@@ -491,7 +491,7 @@ If your local computer doesn't have a powerful GPU you could utilize Google Cola
 Once training is done, upload the latest checkpoint with:
 
 ```bash
-huggingface-cli upload ${HF_USER}/act_so101_test \
+hf upload ${HF_USER}/act_so101_test \
   outputs/train/act_so101_test/checkpoints/last/pretrained_model
 ```
 
@@ -499,7 +499,7 @@ You can also upload intermediate checkpoints with:
 
 ```bash
 CKPT=010000
-huggingface-cli upload ${HF_USER}/act_so101_test${CKPT} \
+hf upload ${HF_USER}/act_so101_test${CKPT} \
   outputs/train/act_so101_test/checkpoints/${CKPT}/pretrained_model
 ```
 

docs/source/lekiwi.mdx

@@ -279,13 +279,13 @@ We use the Hugging Face hub features for uploading your dataset. If you haven't
 Add your token to the CLI by running this command:
 
 ```bash
-huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+hf auth login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
 ```
 
 Then store your Hugging Face repository name in a variable:
 
 ```bash
-HF_USER=$(huggingface-cli whoami | head -n 1)
+HF_USER=$(hf auth whoami | awk -F': *' 'NR==1 {print $2}')
 echo $HF_USER
 ```
 

src/lerobot/datasets/aggregate.py

@@ -21,6 +21,8 @@ from pathlib import Path
 
 import datasets
 import pandas as pd
+import pyarrow as pa
+import pyarrow.parquet as pq
 import tqdm
 
 from lerobot.datasets.compute_stats import aggregate_stats
@@ -35,7 +37,6 @@ from lerobot.datasets.utils import (
     get_file_size_in_mb,
     get_hf_features_from_features,
     get_parquet_file_size_in_mb,
-    to_parquet_with_hf_images,
     update_chunk_file_indices,
     write_info,
     write_stats,
@@ -80,28 +81,41 @@ def validate_all_metadata(all_metadata: list[LeRobotDatasetMetadata]):
     return fps, robot_type, features
 
 
-def update_data_df(df, src_meta, dst_meta):
-    """Updates a data DataFrame with new indices and task mappings for aggregation.
+def update_data_table(table: pa.Table, src_meta, dst_meta) -> pa.Table:
+    """Updates a pyarrow Table with new indices and task mappings for aggregation.
 
     Adjusts episode indices, frame indices, and task indices to account for
     previously aggregated data in the destination dataset.
 
     Args:
-        df: DataFrame containing the data to be updated.
+        table: pyarrow Table containing the data to be updated.
         src_meta: Source dataset metadata.
         dst_meta: Destination dataset metadata.
 
     Returns:
-        pd.DataFrame: Updated DataFrame with adjusted indices.
+        pa.Table: Updated Table with adjusted indices.
     """
+    ep_offset = dst_meta.info["total_episodes"]
+    idx_offset = dst_meta.info["total_frames"]
 
-    df["episode_index"] = df["episode_index"] + dst_meta.info["total_episodes"]
-    df["index"] = df["index"] + dst_meta.info["total_frames"]
+    ep_col = table.column("episode_index")
+    new_ep = pa.array([v + ep_offset for v in ep_col.to_pylist()], type=ep_col.type)
+    table = table.set_column(table.column_names.index("episode_index"), "episode_index", new_ep)
 
-    src_task_names = src_meta.tasks.index.take(df["task_index"].to_numpy())
-    df["task_index"] = dst_meta.tasks.loc[src_task_names, "task_index"].to_numpy()
+    idx_col = table.column("index")
+    new_idx = pa.array([v + idx_offset for v in idx_col.to_pylist()], type=idx_col.type)
+    table = table.set_column(table.column_names.index("index"), "index", new_idx)
 
-    return df
+    old_task_indices = table.column("task_index").to_pylist()
+    src_task_names = src_meta.tasks.index.take(old_task_indices)
+    new_task_indices = dst_meta.tasks.loc[src_task_names, "task_index"].to_numpy().tolist()
+    table = table.set_column(
+        table.column_names.index("task_index"),
+        "task_index",
+        pa.array(new_task_indices, type=table.schema.field("task_index").type),
+    )
+
+    return table
 
 
 def update_meta_data(
@@ -289,7 +303,9 @@ def aggregate_datasets(
 
     logging.info("Find all tasks")
     unique_tasks = pd.concat([m.tasks for m in all_metadata]).index.unique()
-    dst_meta.tasks = pd.DataFrame({"task_index": range(len(unique_tasks))}, index=unique_tasks)
+    dst_meta.tasks = pd.DataFrame(
+        {"task_index": range(len(unique_tasks))}, index=pd.Index(unique_tasks, name="task")
+    )
 
     meta_idx = {"chunk": 0, "file": 0}
     data_idx = {"chunk": 0, "file": 0}
@@ -466,18 +482,13 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si
         src_path = src_meta.root / DEFAULT_DATA_PATH.format(
             chunk_index=src_chunk_idx, file_index=src_file_idx
         )
-        if contains_images:
-            # Use HuggingFace datasets to read source data to preserve image format
-            src_ds = datasets.Dataset.from_parquet(str(src_path))
-            df = src_ds.to_pandas()
-        else:
-            df = pd.read_parquet(src_path)
-        df = update_data_df(df, src_meta, dst_meta)
+        table = pq.read_table(src_path)
+        table = update_data_table(table, src_meta, dst_meta)
 
         # Write data and get the actual destination file it was written to
         # This avoids duplicating the rotation logic here
         data_idx, (dst_chunk, dst_file) = append_or_create_parquet_file(
-            df,
+            table,
             src_path,
             data_idx,
             data_files_size_in_mb,
@@ -552,8 +563,16 @@ def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx):
     return meta_idx
 
 
+def _write_table_with_hf_images(
+    table: pa.Table, path: Path, features: datasets.Features | None = None
+) -> None:
+    """Write a pyarrow Table to parquet with proper HF image encoding."""
+    ds = datasets.Dataset.from_dict(table.to_pydict(), features=features)
+    ds.to_parquet(path)
+
+
 def append_or_create_parquet_file(
-    df: pd.DataFrame,
+    data: pd.DataFrame | pa.Table,
     src_path: Path,
     idx: dict[str, int],
     max_mb: float,
@@ -569,7 +588,7 @@ def append_or_create_parquet_file(
     from becoming too large. Handles both regular parquet files and those containing images.
 
     Args:
-        df: DataFrame to write to the parquet file.
+        data: Data to write, as a pandas DataFrame or pyarrow Table.
         src_path: Path to the source file (used for size estimation).
         idx: Dictionary containing current 'chunk' and 'file' indices.
         max_mb: Maximum allowed file size in MB before rotation.
@@ -583,15 +602,17 @@ def append_or_create_parquet_file(
         tuple: (updated_idx, (dst_chunk, dst_file)) where updated_idx is the index dict
                and (dst_chunk, dst_file) is the actual destination file the data was written to.
     """
+    table = data if isinstance(data, pa.Table) else pa.Table.from_pandas(data)
+
     dst_chunk, dst_file = idx["chunk"], idx["file"]
     dst_path = aggr_root / default_path.format(chunk_index=dst_chunk, file_index=dst_file)
 
     if not dst_path.exists():
         dst_path.parent.mkdir(parents=True, exist_ok=True)
         if contains_images:
-            to_parquet_with_hf_images(df, dst_path, features=hf_features)
+            _write_table_with_hf_images(table, dst_path, features=hf_features)
         else:
-            df.to_parquet(dst_path)
+            pq.write_table(table, dst_path)
         return idx, (dst_chunk, dst_file)
 
     src_size = get_parquet_file_size_in_mb(src_path)
@@ -602,22 +623,17 @@ def append_or_create_parquet_file(
         dst_chunk, dst_file = idx["chunk"], idx["file"]
         new_path = aggr_root / default_path.format(chunk_index=dst_chunk, file_index=dst_file)
         new_path.parent.mkdir(parents=True, exist_ok=True)
-        final_df = df
+        final_table = table
         target_path = new_path
     else:
-        if contains_images:
-            # Use HuggingFace datasets to read existing data to preserve image format
-            existing_ds = datasets.Dataset.from_parquet(str(dst_path))
-            existing_df = existing_ds.to_pandas()
-        else:
-            existing_df = pd.read_parquet(dst_path)
-        final_df = pd.concat([existing_df, df], ignore_index=True)
+        existing_table = pq.read_table(dst_path)
+        final_table = pa.concat_tables([existing_table, table], promote_options="permissive")
         target_path = dst_path
 
     if contains_images:
-        to_parquet_with_hf_images(final_df, target_path, features=hf_features)
+        _write_table_with_hf_images(final_table, target_path, features=hf_features)
     else:
-        final_df.to_parquet(target_path)
+        pq.write_table(final_table, target_path)
 
     return idx, (dst_chunk, dst_file)
 

src/lerobot/datasets/dataset_tools.py

@@ -32,6 +32,9 @@ from pathlib import Path
 import datasets
 import numpy as np
 import pandas as pd
+import pyarrow as pa
+import pyarrow.compute as pc
+import pyarrow.dataset as pa_ds
 import pyarrow.parquet as pq
 import torch
 from tqdm import tqdm
@@ -89,8 +92,8 @@ def delete_episodes(
     Args:
         dataset: The source LeRobotDataset.
         episode_indices: List of episode indices to delete.
-        output_dir: Directory to save the new dataset. If None, uses default location.
-        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.
+        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
+        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
     """
     if not episode_indices:
         raise ValueError("No episodes to delete")
@@ -152,7 +155,7 @@ def split_dataset(
         dataset: The source LeRobotDataset to split.
         splits: Either a dict mapping split names to episode indices, or a dict mapping
                 split names to fractions (must sum to <= 1.0).
-        output_dir: Base directory for output datasets. If None, uses default location.
+        output_dir: Root directory where the split datasets will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id.
 
     Examples:
       Split by specific episodes
@@ -243,8 +246,8 @@ def merge_datasets(
 
     Args:
         datasets: List of LeRobotDatasets to merge.
-        output_repo_id: Repository ID for the merged dataset.
-        output_dir: Directory to save the merged dataset. If None, uses default location.
+        output_repo_id: Merged dataset identifier.
+        output_dir: Root directory where the merged dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/output_repo_id.
     """
     if not datasets:
         raise ValueError("No datasets to merge")
@@ -288,8 +291,8 @@ def modify_features(
         dataset: The source LeRobotDataset.
         add_features: Optional dict mapping feature names to (feature_values, feature_info) tuples.
         remove_features: Optional feature name(s) to remove. Can be a single string or list.
-        output_dir: Directory to save the new dataset. If None, uses default location.
-        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.
+        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
+        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
 
     Returns:
         New dataset with features modified.
@@ -390,8 +393,8 @@ def add_features(
     Args:
         dataset: The source LeRobotDataset.
         features: Dictionary mapping feature names to (feature_values, feature_info) tuples.
-        output_dir: Directory to save the new dataset. If None, uses default location.
-        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.
+        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
+        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
 
     Returns:
         New dataset with all features added.
@@ -427,8 +430,8 @@ def remove_feature(
     Args:
         dataset: The source LeRobotDataset.
         feature_names: Name(s) of features to remove. Can be a single string or list.
-        output_dir: Directory to save the new dataset. If None, uses default location.
-        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.
+        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
+        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
 
     Returns:
         New dataset with features removed.
@@ -496,13 +499,16 @@ def _copy_and_reindex_data(
     global_index = 0
     episode_data_metadata: dict[int, dict] = {}
 
+    episode_keys = list(episode_mapping.keys())
+    ep_filter = pa_ds.field("episode_index").isin(episode_keys)
+
     if dst_meta.tasks is None:
-        all_task_indices = set()
+        all_task_indices: set[int] = set()
         for src_path in file_to_episodes:
-            df = pd.read_parquet(src_dataset.root / src_path)
-            mask = df["episode_index"].isin(list(episode_mapping.keys()))
-            task_series: pd.Series = df[mask]["task_index"]
-            all_task_indices.update(task_series.unique().tolist())
+            table = pq.read_table(
+                src_dataset.root / src_path, columns=["episode_index", "task_index"], filters=ep_filter
+            )
+            all_task_indices.update(pc.unique(table.column("task_index")).to_pylist())
         tasks = [src_dataset.meta.tasks.iloc[idx].name for idx in all_task_indices]
         dst_meta.save_episode_tasks(list(set(tasks)))
 
@@ -514,52 +520,41 @@ def _copy_and_reindex_data(
             task_mapping[old_task_idx] = new_task_idx
 
     for src_path in tqdm(sorted(file_to_episodes.keys()), desc="Processing data files"):
-        df = pd.read_parquet(src_dataset.root / src_path)
-
-        all_episodes_in_file = set(df["episode_index"].unique())
+        table = pq.read_table(src_dataset.root / src_path, filters=ep_filter)
         episodes_to_keep = file_to_episodes[src_path]
 
-        if all_episodes_in_file == episodes_to_keep:
-            df["episode_index"] = df["episode_index"].replace(episode_mapping)
-            df["index"] = range(global_index, global_index + len(df))
-            df["task_index"] = df["task_index"].replace(task_mapping)
+        if table.num_rows == 0:
+            continue
 
-            first_ep_old_idx = min(episodes_to_keep)
-            src_ep = src_dataset.meta.episodes[first_ep_old_idx]
-            chunk_idx = src_ep["data/chunk_index"]
-            file_idx = src_ep["data/file_index"]
-        else:
-            mask = df["episode_index"].isin(list(episode_mapping.keys()))
-            df = df[mask].copy().reset_index(drop=True)
+        table = _replace_column_values(table, "episode_index", episode_mapping)
+        col_pos = table.column_names.index("index")
+        new_indices = pa.array(range(global_index, global_index + table.num_rows), type=pa.int64())
+        table = table.set_column(col_pos, "index", new_indices)
+        table = _replace_column_values(table, "task_index", task_mapping)
 
-            if len(df) == 0:
-                continue
-
-            df["episode_index"] = df["episode_index"].replace(episode_mapping)
-            df["index"] = range(global_index, global_index + len(df))
-            df["task_index"] = df["task_index"].replace(task_mapping)
-
-            first_ep_old_idx = min(episodes_to_keep)
-            src_ep = src_dataset.meta.episodes[first_ep_old_idx]
-            chunk_idx = src_ep["data/chunk_index"]
-            file_idx = src_ep["data/file_index"]
+        first_ep_old_idx = min(episodes_to_keep)
+        src_ep = src_dataset.meta.episodes[first_ep_old_idx]
+        chunk_idx = src_ep["data/chunk_index"]
+        file_idx = src_ep["data/file_index"]
 
         dst_path = dst_meta.root / DEFAULT_DATA_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
         dst_path.parent.mkdir(parents=True, exist_ok=True)
 
-        _write_parquet(df, dst_path, dst_meta)
+        _write_parquet(table, dst_path, dst_meta)
 
+        ep_col = table.column("episode_index").to_pylist()
+        idx_col = table.column("index").to_pylist()
         for ep_old_idx in episodes_to_keep:
             ep_new_idx = episode_mapping[ep_old_idx]
-            ep_df = df[df["episode_index"] == ep_new_idx]
+            ep_indices = [idx_col[i] for i, e in enumerate(ep_col) if e == ep_new_idx]
             episode_data_metadata[ep_new_idx] = {
                 "data/chunk_index": chunk_idx,
                 "data/file_index": file_idx,
-                "dataset_from_index": int(ep_df["index"].min()),
-                "dataset_to_index": int(ep_df["index"].max() + 1),
+                "dataset_from_index": min(ep_indices),
+                "dataset_to_index": max(ep_indices) + 1,
             }
 
-        global_index += len(df)
+        global_index += table.num_rows
 
     return episode_data_metadata
 
@@ -910,15 +905,39 @@ def _copy_and_reindex_episodes_metadata(
     write_stats(filtered_stats, dst_meta.root)
 
 
-def _write_parquet(df: pd.DataFrame, path: Path, meta: LeRobotDatasetMetadata) -> None:
-    """Write DataFrame to parquet
+def _replace_column_values(table: pa.Table, column: str, mapping: dict) -> pa.Table:
+    """Replace values in a pyarrow Table column using a mapping dict."""
+    old_values = table.column(column).to_pylist()
+    new_values = [mapping.get(v, v) for v in old_values]
+    col_pos = table.column_names.index(column)
+    return table.set_column(col_pos, column, pa.array(new_values, type=table.schema.field(column).type))
+
+
+def _write_parquet(
+    data: pd.DataFrame | pa.Table | dict, path: Path, meta: LeRobotDatasetMetadata
+) -> None:
+    """Write data to parquet.
 
     This ensures images are properly embedded and the file can be loaded correctly by HF datasets.
+
+    Args:
+        data: Input data as a pandas DataFrame, pyarrow Table, or dict of lists.
+        path: Destination parquet file path.
+        meta: Dataset metadata for feature schema.
     """
     from lerobot.datasets.utils import embed_images, get_hf_features_from_features
 
+    if isinstance(data, pd.DataFrame):
+        data_dict = data.to_dict(orient="list")
+    elif isinstance(data, pa.Table):
+        data_dict = data.to_pydict()
+    elif isinstance(data, dict):
+        data_dict = data
+    else:
+        raise TypeError(f"Unsupported data type: {type(data)}")
+
     hf_features = get_hf_features_from_features(meta.features)
-    ep_dataset = datasets.Dataset.from_dict(df.to_dict(orient="list"), features=hf_features, split="train")
+    ep_dataset = datasets.Dataset.from_dict(data_dict, features=hf_features, split="train")
 
     if len(meta.image_keys) > 0:
         ep_dataset = embed_images(ep_dataset)
@@ -1475,7 +1494,9 @@ def modify_tasks(
 
     # Collect all unique tasks and create new task mapping
     unique_tasks = sorted(set(episode_to_task.values()))
-    new_task_df = pd.DataFrame({"task_index": list(range(len(unique_tasks)))}, index=unique_tasks)
+    new_task_df = pd.DataFrame(
+        {"task_index": list(range(len(unique_tasks)))}, index=pd.Index(unique_tasks, name="task")
+    )
     task_to_index = {task: idx for idx, task in enumerate(unique_tasks)}
 
     logging.info(f"Modifying tasks in {dataset.repo_id}")
@@ -1529,7 +1550,7 @@ def modify_tasks(
 
 def convert_image_to_video_dataset(
     dataset: LeRobotDataset,
-    output_dir: Path,
+    output_dir: Path | None = None,
     repo_id: str | None = None,
     vcodec: str = "libsvtav1",
     pix_fmt: str = "yuv420p",
@@ -1548,8 +1569,8 @@ def convert_image_to_video_dataset(
 
     Args:
         dataset: The source LeRobot dataset with images
-        output_dir: Directory to save the new video dataset
-        repo_id: Repository ID for the new dataset (default: original_id + "_video")
+        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
+        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
         vcodec: Video codec (default: libsvtav1)
         pix_fmt: Pixel format (default: yuv420p)
         g: Group of pictures size (default: 2)
@@ -1600,6 +1621,7 @@ def convert_image_to_video_dataset(
             # Video info will be updated after episodes are encoded
 
     # Create new metadata for video dataset
+    output_dir = Path(output_dir) if output_dir is not None else HF_LEROBOT_HOME / repo_id
     new_meta = LeRobotDatasetMetadata.create(
         repo_id=repo_id,
         fps=dataset.meta.fps,

src/lerobot/datasets/lerobot_dataset.py

@@ -314,7 +314,7 @@ class LeRobotDatasetMetadata:
         if self.tasks is None:
             new_tasks = tasks
             task_indices = range(len(tasks))
-            self.tasks = pd.DataFrame({"task_index": task_indices}, index=tasks)
+            self.tasks = pd.DataFrame({"task_index": task_indices}, index=pd.Index(tasks, name="task"))
         else:
             new_tasks = [task for task in tasks if task not in self.tasks.index]
             new_task_indices = range(len(self.tasks), len(self.tasks) + len(new_tasks))

src/lerobot/datasets/utils.py

@@ -341,6 +341,7 @@ def write_tasks(tasks: pandas.DataFrame, local_dir: Path) -> None:
 
 def load_tasks(local_dir: Path) -> pandas.DataFrame:
     tasks = pd.read_parquet(local_dir / DEFAULT_TASKS_PATH)
+    tasks.index.name = "task"
     return tasks
 
 

src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py

@@ -36,8 +36,11 @@ Convert a local dataset (works in place):
 ```bash
 python src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py \
     --repo-id=lerobot/pusht \
-    --root=/path/to/local/dataset/directory
+    --root=/path/to/local/dataset/directory \
     --push-to-hub=false
+
+N.B. Path semantics (v2): --root is the exact dataset folder containing
+meta/, data/, videos/. When omitted, defaults to $HF_LEROBOT_HOME/{repo_id}.
 ```
 
 """
@@ -105,7 +108,7 @@ episodes.jsonl
 {"episode_index": 1, "tasks": ["Put the blue block in the green bowl"], "length": 266}
 
 NEW
-meta/episodes/chunk-000/episodes_000.parquet
+meta/episodes/chunk-000/file_000.parquet
 episode_index | video_chunk_index | video_file_index | data_chunk_index | data_file_index | tasks | length
 -------------------------
 OLD
@@ -113,15 +116,16 @@ tasks.jsonl
 {"task_index": 1, "task": "Put the blue block in the green bowl"}
 
 NEW
-meta/tasks/chunk-000/file_000.parquet
+meta/tasks.parquet
 task_index | task
 -------------------------
 OLD
 episodes_stats.jsonl
+{"episode_index": 1, "stats": {"feature_name": {"min": ..., "max": ..., "mean": ..., "std": ..., "count": ...}}}
 
 NEW
-meta/episodes_stats/chunk-000/file_000.parquet
-episode_index | mean | std | min | max
+meta/episodes/chunk-000/file_000.parquet
+episode_index | feature_name/min | feature_name/max | feature_name/mean | feature_name/std | feature_name/count
 -------------------------
 UPDATE
 meta/info.json
@@ -170,7 +174,7 @@ def convert_tasks(root, new_root):
     tasks, _ = legacy_load_tasks(root)
     task_indices = tasks.keys()
     task_strings = tasks.values()
-    df_tasks = pd.DataFrame({"task_index": task_indices}, index=task_strings)
+    df_tasks = pd.DataFrame({"task_index": task_indices}, index=pd.Index(task_strings, name="task"))
     write_tasks(df_tasks, new_root)
 
 
@@ -201,7 +205,6 @@ def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int):
 
     image_keys = get_image_keys(root)
 
-    ep_idx = 0
     chunk_idx = 0
     file_idx = 0
     size_in_mb = 0
@@ -211,9 +214,24 @@ def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int):
 
     logging.info(f"Converting data files from {len(ep_paths)} episodes")
 
-    for ep_path in tqdm.tqdm(ep_paths, desc="convert data files"):
+    for ep_idx, ep_path in enumerate(tqdm.tqdm(ep_paths, desc="convert data files")):
         ep_size_in_mb = get_parquet_file_size_in_mb(ep_path)
         ep_num_frames = get_parquet_num_frames(ep_path)
+
+        # Check if we need to start a new file BEFORE creating metadata
+        if size_in_mb + ep_size_in_mb >= data_file_size_in_mb and len(paths_to_cat) > 0:
+            # Write the accumulated data files
+            concat_data_files(paths_to_cat, new_root, chunk_idx, file_idx, image_keys)
+
+            # Move to next file
+            chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, DEFAULT_CHUNK_SIZE)
+
+            # Reset for the next file
+            size_in_mb = 0
+            num_frames += ep_num_frames  # Still need to accumulate total frames
+            paths_to_cat = []
+
+        # Now create metadata with correct chunk/file indices
         ep_metadata = {
             "episode_index": ep_idx,
             "data/chunk_index": chunk_idx,
@@ -224,20 +242,7 @@ def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int):
         size_in_mb += ep_size_in_mb
         num_frames += ep_num_frames
         episodes_metadata.append(ep_metadata)
-        ep_idx += 1
-
-        if size_in_mb < data_file_size_in_mb:
-            paths_to_cat.append(ep_path)
-            continue
-
-        if paths_to_cat:
-            concat_data_files(paths_to_cat, new_root, chunk_idx, file_idx, image_keys)
-
-        # Reset for the next file
-        size_in_mb = ep_size_in_mb
-        paths_to_cat = [ep_path]
-
-        chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, DEFAULT_CHUNK_SIZE)
+        paths_to_cat.append(ep_path)
 
     # Write remaining data if any
     if paths_to_cat:
@@ -469,7 +474,7 @@ def convert_dataset(
 
     # Set root based on whether local dataset path is provided
     use_local_dataset = False
-    root = HF_LEROBOT_HOME / repo_id if root is None else Path(root) / repo_id
+    root = HF_LEROBOT_HOME / repo_id if root is None else Path(root)
     if root.exists():
         validate_local_dataset_version(root)
         use_local_dataset = True
@@ -553,7 +558,7 @@ if __name__ == "__main__":
         "--root",
         type=str,
         default=None,
-        help="Local directory to use for downloading/writing the dataset.",
+        help="Local directory to use for downloading/writing the dataset. Defaults to $HF_LEROBOT_HOME/repo_id.",
     )
     parser.add_argument(
         "--push-to-hub",

src/lerobot/scripts/lerobot_dataset_viz.py

@@ -132,10 +132,13 @@ def visualize_dataset(
 
     logging.info("Logging to Rerun")
 
+    first_index = None
     for batch in tqdm.tqdm(dataloader, total=len(dataloader)):
+        if first_index is None:
+            first_index = batch["index"][0].item()
         # iterate over the batch
         for i in range(len(batch["index"])):
-            rr.set_time("frame_index", sequence=batch["frame_index"][i].item())
+            rr.set_time("frame_index", sequence=batch["index"][i].item() - first_index)
             rr.set_time("timestamp", timestamp=batch["timestamp"][i].item())
 
             # display each camera image

src/lerobot/scripts/lerobot_edit_dataset.py

@@ -21,6 +21,9 @@ This script allows you to delete episodes, split datasets, merge datasets,
 remove features, modify tasks, and convert image datasets to video format.
 When new_repo_id is specified, creates a new dataset.
 
+Path semantics (v2): --root and --new_root are exact dataset folders containing
+meta/, data/, videos/. When omitted, defaults to $HF_LEROBOT_HOME/{repo_id}.
+
 Usage Examples:
 
 Delete episodes 0, 2, and 5 from a dataset:
@@ -29,16 +32,31 @@ Delete episodes 0, 2, and 5 from a dataset:
         --operation.type delete_episodes \
         --operation.episode_indices "[0, 2, 5]"
 
-Delete episodes and save to a new dataset:
+Delete episodes from a local dataset at a specific path:
     lerobot-edit-dataset \
         --repo_id lerobot/pusht \
-        --new_repo_id lerobot/pusht_filtered \
+        --root /path/to/pusht \
         --operation.type delete_episodes \
         --operation.episode_indices "[0, 2, 5]"
 
-Split dataset by fractions:
+Delete episodes and save to a new dataset at a specific path and with a new repo_id:
     lerobot-edit-dataset \
         --repo_id lerobot/pusht \
+        --new_repo_id lerobot/pusht_filtered \
+        --new_root /path/to/pusht_filtered \
+        --operation.type delete_episodes \
+        --operation.episode_indices "[0, 2, 5]"
+
+Split dataset by fractions (pusht_train, pusht_val):
+    lerobot-edit-dataset \
+        --repo_id lerobot/pusht \
+        --operation.type split \
+        --operation.splits '{"train": 0.8, "val": 0.2}'
+
+Split dataset by fractions and save split datasets to a specific folder (base_folder/train, base_folder/val):
+    lerobot-edit-dataset \
+        --repo_id lerobot/pusht \
+        --new_root /path/to/base_folder \
         --operation.type split \
         --operation.splits '{"train": 0.8, "val": 0.2}'
 
@@ -56,15 +74,29 @@ Split into more than two splits:
 
 Merge multiple datasets:
     lerobot-edit-dataset \
-        --repo_id lerobot/pusht_merged \
+        --new_repo_id lerobot/pusht_merged \
         --operation.type merge \
         --operation.repo_ids "['lerobot/pusht_train', 'lerobot/pusht_val']"
 
+Merge multiple datasets to a specific output path:
+    lerobot-edit-dataset \
+        --new_repo_id lerobot/pusht_merged \
+        --new_root /path/to/pusht_merged \
+        --operation.type merge \
+        --operation.repo_ids "['lerobot/pusht_train', 'lerobot/pusht_val']"
+
+Merge multiple datasets from a list of local dataset paths:
+    lerobot-edit-dataset \
+        --new_repo_id lerobot/pusht_merged \
+        --operation.type merge \
+        --operation.repo_ids "['pusht_train', 'pusht_val']" \
+        --operation.roots "['/path/to/pusht_train', '/path/to/pusht_val']"
+
 Remove camera feature:
     lerobot-edit-dataset \
         --repo_id lerobot/pusht \
         --operation.type remove_feature \
-        --operation.feature_names "['observation.images.top']"
+        --operation.feature_names "['observation.image']"
 
 Modify tasks - set a single task for all episodes (WARNING: modifies in-place):
     lerobot-edit-dataset \
@@ -88,8 +120,8 @@ Modify tasks - set default task with overrides for specific episodes (WARNING: m
 Convert image dataset to video format and save locally:
     lerobot-edit-dataset \
         --repo_id lerobot/pusht_image \
-        --operation.type convert_image_to_video \
-        --operation.output_dir /path/to/output/pusht_video
+        --new_root /path/to/output/pusht_video \
+        --operation.type convert_image_to_video
 
 Convert image dataset to video format and save with new repo_id:
     lerobot-edit-dataset \
@@ -167,6 +199,7 @@ class SplitConfig(OperationConfig):
 @dataclass
 class MergeConfig(OperationConfig):
     repo_ids: list[str] | None = None
+    roots: list[str] | None = None
 
 
 @OperationConfig.register_subclass("remove_feature")
@@ -200,42 +233,46 @@ class ConvertImageToVideoConfig(OperationConfig):
 @OperationConfig.register_subclass("info")
 @dataclass
 class InfoConfig(OperationConfig):
-    type: str = "info"
     show_features: bool = False
 
 
 @dataclass
 class EditDatasetConfig:
-    repo_id: str
+    # Operation configuration.
     operation: OperationConfig
-    # Parent cache directory. Each dataset lives at root/{repo_id}. If None, defaults to HF_LEROBOT_HOME.
+    # Input dataset identifier. Always required unless for Merge operation.
+    repo_id: str | None = None
+    # Root directory where the input dataset is stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id.
     root: str | None = None
+    # Edited dataset identifier. When both new_repo_id (resp. new_root) and repo_id (resp. root) are identical, modifications are applied in-place and a backup of the original dataset is created. Required for Merge operation.
     new_repo_id: str | None = None
+    # Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/new_repo_id. For Split operation, this is the base directory for the split datasets.
+    new_root: str | None = None
+    # Upload dataset to Hugging Face hub.
     push_to_hub: bool = False
 
 
-def _resolve_root(root: str | None, repo_id: str) -> Path | None:
-    """Translate a parent cache directory into the exact dataset path expected by LeRobotDataset."""
-    return Path(root) / repo_id if root else None
+def get_output_path(
+    repo_id: str,
+    new_repo_id: str | None,
+    root: Path | str | None,
+    new_root: Path | str | None,
+) -> tuple[str, Path]:
+    input_path = Path(root) if root else HF_LEROBOT_HOME / repo_id
 
+    output_repo_id = new_repo_id if new_repo_id else repo_id
+    output_path = Path(new_root) if new_root else HF_LEROBOT_HOME / output_repo_id
 
-def get_output_path(repo_id: str, new_repo_id: str | None, root: Path | None) -> tuple[str, Path]:
-    if new_repo_id:
-        output_repo_id = new_repo_id
-        output_dir = root / new_repo_id if root else HF_LEROBOT_HOME / new_repo_id
-    else:
-        output_repo_id = repo_id
-        dataset_path = root / repo_id if root else HF_LEROBOT_HOME / repo_id
-        old_path = Path(str(dataset_path) + "_old")
+    # In case of in-place modification, create a backup of the original dataset (if it exists)
+    if output_path == input_path:
+        backup_path = input_path.with_name(input_path.name + "_old")
 
-        if dataset_path.exists():
-            if old_path.exists():
-                shutil.rmtree(old_path)
-            shutil.move(str(dataset_path), str(old_path))
+        if input_path.exists():
+            if backup_path.exists():
+                shutil.rmtree(backup_path)
+            shutil.move(input_path, backup_path)
 
-        output_dir = dataset_path
-
-    return output_repo_id, output_dir
+    return output_repo_id, output_path
 
 
 def handle_delete_episodes(cfg: EditDatasetConfig) -> None:
@@ -245,13 +282,17 @@ def handle_delete_episodes(cfg: EditDatasetConfig) -> None:
     if not cfg.operation.episode_indices:
         raise ValueError("episode_indices must be specified for delete_episodes operation")
 
-    dataset = LeRobotDataset(cfg.repo_id, root=_resolve_root(cfg.root, cfg.repo_id))
+    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
     output_repo_id, output_dir = get_output_path(
-        cfg.repo_id, cfg.new_repo_id, Path(cfg.root) if cfg.root else None
+        cfg.repo_id,
+        new_repo_id=cfg.new_repo_id,
+        root=cfg.root,
+        new_root=cfg.new_root,
     )
 
-    if cfg.new_repo_id is None:
-        dataset.root = Path(str(dataset.root) + "_old")
+    # In case of in-place modification, make the dataset point to the backup directory
+    if output_dir == dataset.root:
+        dataset.root = dataset.root.with_name(dataset.root.name + "_old")
 
     logging.info(f"Deleting episodes {cfg.operation.episode_indices} from {cfg.repo_id}")
     new_dataset = delete_episodes(
@@ -278,19 +319,27 @@ def handle_split(cfg: EditDatasetConfig) -> None:
             "splits dict must be specified with split names as keys and fractions/episode lists as values"
         )
 
-    dataset = LeRobotDataset(cfg.repo_id, root=_resolve_root(cfg.root, cfg.repo_id))
+    if cfg.new_repo_id is not None:
+        logging.warning(
+            "split uses the original dataset identifier --repo_id to generate split names. The --new_repo_id parameter is ignored."
+        )
+
+    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
 
     logging.info(f"Splitting dataset {cfg.repo_id} with splits: {cfg.operation.splits}")
-    split_datasets = split_dataset(dataset, splits=cfg.operation.splits)
+    split_datasets = split_dataset(
+        dataset,
+        splits=cfg.operation.splits,
+        output_dir=cfg.new_root,
+    )
 
     for split_name, split_ds in split_datasets.items():
-        split_repo_id = f"{cfg.repo_id}_{split_name}"
         logging.info(
             f"{split_name}: {split_ds.meta.total_episodes} episodes, {split_ds.meta.total_frames} frames"
         )
 
         if cfg.push_to_hub:
-            logging.info(f"Pushing {split_name} split to hub as {split_repo_id}")
+            logging.info(f"Pushing {split_name} split to hub as {split_ds.repo_id}")
             LeRobotDataset(split_ds.repo_id, root=split_ds.root).push_to_hub()
 
 
@@ -301,20 +350,29 @@ def handle_merge(cfg: EditDatasetConfig) -> None:
     if not cfg.operation.repo_ids:
         raise ValueError("repo_ids must be specified for merge operation")
 
-    if not cfg.repo_id:
-        raise ValueError("repo_id must be specified as the output repository for merged dataset")
+    if cfg.repo_id is not None or cfg.root is not None:
+        logging.warning(
+            "merge uses --new_repo_id and --new_root for the merged dataset. The --repo_id and --root parameters are ignored."
+        )
 
-    logging.info(f"Loading {len(cfg.operation.repo_ids)} datasets to merge")
-    datasets = [
-        LeRobotDataset(repo_id, root=_resolve_root(cfg.root, repo_id)) for repo_id in cfg.operation.repo_ids
-    ]
+    if cfg.operation.roots:
+        if len(cfg.operation.roots) != len(cfg.operation.repo_ids):
+            raise ValueError("repo_ids and roots must have the same length for merge operation")
+        logging.info(f"Loading {len(cfg.operation.roots)} datasets to merge")
+        datasets = [
+            LeRobotDataset(repo_id=repo_id, root=root)
+            for repo_id, root in zip(cfg.operation.repo_ids, cfg.operation.roots, strict=True)
+        ]
+    else:
+        logging.info(f"Loading {len(cfg.operation.repo_ids)} datasets to merge")
+        datasets = [LeRobotDataset(repo_id) for repo_id in cfg.operation.repo_ids]
 
-    output_dir = Path(cfg.root) / cfg.repo_id if cfg.root else HF_LEROBOT_HOME / cfg.repo_id
+    output_dir = Path(cfg.new_root) if cfg.new_root else HF_LEROBOT_HOME / cfg.new_repo_id
 
-    logging.info(f"Merging datasets into {cfg.repo_id}")
+    logging.info(f"Merging datasets into {cfg.new_repo_id}")
     merged_dataset = merge_datasets(
         datasets,
-        output_repo_id=cfg.repo_id,
+        output_repo_id=cfg.new_repo_id,
         output_dir=output_dir,
     )
 
@@ -324,7 +382,7 @@ def handle_merge(cfg: EditDatasetConfig) -> None:
     )
 
     if cfg.push_to_hub:
-        logging.info(f"Pushing to hub as {cfg.repo_id}")
+        logging.info(f"Pushing to hub as {cfg.new_repo_id}")
         LeRobotDataset(merged_dataset.repo_id, root=output_dir).push_to_hub()
 
 
@@ -335,13 +393,17 @@ def handle_remove_feature(cfg: EditDatasetConfig) -> None:
     if not cfg.operation.feature_names:
         raise ValueError("feature_names must be specified for remove_feature operation")
 
-    dataset = LeRobotDataset(cfg.repo_id, root=_resolve_root(cfg.root, cfg.repo_id))
+    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
     output_repo_id, output_dir = get_output_path(
-        cfg.repo_id, cfg.new_repo_id, Path(cfg.root) if cfg.root else None
+        cfg.repo_id,
+        new_repo_id=cfg.new_repo_id,
+        root=cfg.root,
+        new_root=cfg.new_root,
     )
 
-    if cfg.new_repo_id is None:
-        dataset.root = Path(str(dataset.root) + "_old")
+    # In case of in-place modification, make the dataset point to the backup directory
+    if output_dir == dataset.root:
+        dataset.root = dataset.root.with_name(dataset.root.name + "_old")
 
     logging.info(f"Removing features {cfg.operation.feature_names} from {cfg.repo_id}")
     new_dataset = remove_feature(
@@ -369,11 +431,12 @@ def handle_modify_tasks(cfg: EditDatasetConfig) -> None:
     if new_task is None and episode_tasks_raw is None:
         raise ValueError("Must specify at least one of new_task or episode_tasks for modify_tasks operation")
 
-    # Warn about in-place modification behavior
-    if cfg.new_repo_id is not None:
-        logging.warning("modify_tasks modifies datasets in-place. The --new_repo_id parameter is ignored.")
+    if cfg.new_repo_id is not None or cfg.new_root is not None:
+        logging.warning(
+            "modify_tasks modifies datasets in-place. The --new_repo_id and --new_root parameters are ignored."
+        )
 
-    dataset = LeRobotDataset(cfg.repo_id, root=_resolve_root(cfg.root, cfg.repo_id))
+    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
     logging.warning(f"Modifying dataset in-place at {dataset.root}. Original data will be overwritten.")
 
     # Convert episode_tasks keys from string to int if needed (CLI passes strings)
@@ -404,35 +467,33 @@ def handle_modify_tasks(cfg: EditDatasetConfig) -> None:
 def handle_convert_image_to_video(cfg: EditDatasetConfig) -> None:
     # Note: Parser may create any config type with the right fields, so we access fields directly
     # instead of checking isinstance()
-    dataset = LeRobotDataset(cfg.repo_id, root=_resolve_root(cfg.root, cfg.repo_id))
+    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
 
     # Determine output directory and repo_id
-    # Priority: 1) new_repo_id, 2) operation.output_dir, 3) auto-generated name
+    # Priority: 1) new_root, 2) new_repo_id, 3) operation.output_dir, 4) auto-generated name
     output_dir_config = getattr(cfg.operation, "output_dir", None)
+    if output_dir_config:
+        logging.warning(
+            "--operation.output_dir is deprecated and will be removed in future versions. "
+            "Please use --new_root instead."
+        )
 
-    if cfg.new_repo_id:
-        # Use new_repo_id for both local storage and hub push
+    if cfg.new_root:
+        output_dir = Path(cfg.new_root)
+        output_repo_id = cfg.new_repo_id or f"{cfg.repo_id}_video"
+        logging.info(f"Saving to new_root: {output_dir} as {output_repo_id}")
+    elif cfg.new_repo_id:
         output_repo_id = cfg.new_repo_id
-        # Place new dataset as a sibling to the original dataset
-        # Get the parent of the actual dataset root (not cfg.root which might be the lerobot cache dir)
-        # Extract just the dataset name (after last slash) for the local directory
-        local_dir_name = cfg.new_repo_id.split("/")[-1]
-        output_dir = dataset.root.parent / local_dir_name
+        output_dir = HF_LEROBOT_HOME / cfg.new_repo_id
         logging.info(f"Saving to new dataset: {cfg.new_repo_id} at {output_dir}")
     elif output_dir_config:
-        # Use custom output directory for local-only storage
         output_dir = Path(output_dir_config)
-        # Extract repo name from output_dir for the dataset
         output_repo_id = output_dir.name
-        logging.info(f"Saving to local directory: {output_dir}")
+        logging.info(f"Saving to local directory: {output_dir} as {output_repo_id}")
     else:
-        # Auto-generate name: append "_video" to original repo_id
         output_repo_id = f"{cfg.repo_id}_video"
-        # Place new dataset as a sibling to the original dataset
-        # Extract just the dataset name (after last slash) for the local directory
-        local_dir_name = output_repo_id.split("/")[-1]
-        output_dir = dataset.root.parent / local_dir_name
-        logging.info(f"Saving to auto-generated location: {output_dir}")
+        output_dir = HF_LEROBOT_HOME / output_repo_id
+        logging.info(f"Saving to auto-generated location: {output_dir} as {output_repo_id}")
 
     logging.info(f"Converting dataset {cfg.repo_id} to video format")
 
@@ -481,7 +542,7 @@ def handle_info(cfg: EditDatasetConfig):
     if not isinstance(cfg.operation, InfoConfig):
         raise ValueError("Operation config must be InfoConfig")
 
-    dataset = LeRobotDataset(cfg.repo_id, root=_resolve_root(cfg.root, cfg.repo_id))
+    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
     sys.stdout.write(f"======Info {dataset.meta.repo_id}\n")
     sys.stdout.write(f"Repository ID: {dataset.meta.repo_id} \n")
     sys.stdout.write(f"Total episode: {dataset.meta.total_episodes} \n")
@@ -507,8 +568,20 @@ def handle_info(cfg: EditDatasetConfig):
         sys.stdout.write(f"{feature_dump_str}\n")
 
 
+def _validate_config(cfg: EditDatasetConfig) -> None:
+    if isinstance(cfg.operation, MergeConfig):
+        if not cfg.new_repo_id:
+            raise ValueError("--new_repo_id is required for merge operation (the merged dataset identifier)")
+    else:
+        if not cfg.repo_id:
+            raise ValueError(
+                f"--repo_id is required for {cfg.operation.type} operation (the input dataset identifier)"
+            )
+
+
 @parser.wrap()
 def edit_dataset(cfg: EditDatasetConfig) -> None:
+    _validate_config(cfg)
     operation_type = cfg.operation.type
 
     if operation_type == "delete_episodes":

tests/fixtures/dataset_factories.py

@@ -222,7 +222,7 @@ def tasks_factory():
     def _create_tasks(total_tasks: int = 3) -> pd.DataFrame:
         ids = list(range(total_tasks))
         tasks = [f"Perform action {i}." for i in ids]
-        df = pd.DataFrame({"task_index": ids}, index=tasks)
+        df = pd.DataFrame({"task_index": ids}, index=pd.Index(tasks, name="task"))
         return df
 
     return _create_tasks

tests/scripts/test_edit_dataset_parsing.py

@@ -27,6 +27,7 @@ from lerobot.scripts.lerobot_edit_dataset import (
     OperationConfig,
     RemoveFeatureConfig,
     SplitConfig,
+    _validate_config,
 )
 
 
@@ -51,11 +52,23 @@ class TestOperationTypeParsing:
         ],
     )
     def test_operation_type_resolves_correct_class(self, type_name, expected_cls):
-        cfg = parse_cfg(["--repo_id", "test/repo", "--operation.type", type_name])
+        cfg = parse_cfg(
+            ["--repo_id", "test/repo", "--new_repo_id", "test/merged", "--operation.type", type_name]
+        )
         assert isinstance(cfg.operation, expected_cls), (
             f"Expected {expected_cls.__name__}, got {type(cfg.operation).__name__}"
         )
 
+    def test_merge_requires_new_repo_id(self):
+        cfg = parse_cfg(["--operation.type", "merge"])
+        with pytest.raises(ValueError, match="--new_repo_id is required for merge"):
+            _validate_config(cfg)
+
+    def test_non_merge_requires_repo_id(self):
+        cfg = parse_cfg(["--operation.type", "delete_episodes"])
+        with pytest.raises(ValueError, match="--repo_id is required for delete_episodes"):
+            _validate_config(cfg)
+
     @pytest.mark.parametrize(
         "type_name, expected_cls",
         [
@@ -69,6 +82,8 @@ class TestOperationTypeParsing:
         ],
     )
     def test_get_choice_name_roundtrips(self, type_name, expected_cls):
-        cfg = parse_cfg(["--repo_id", "test/repo", "--operation.type", type_name])
+        cfg = parse_cfg(
+            ["--repo_id", "test/repo", "--new_repo_id", "test/merged", "--operation.type", type_name]
+        )
         resolved_name = OperationConfig.get_choice_name(type(cfg.operation))
         assert resolved_name == type_name