add high/low/normal level annotation

src/lerobot/datasets/lerobot_dataset.py

@@ -57,6 +57,7 @@ from lerobot.datasets.utils import (
     load_info,
     load_nested_dataset,
     load_stats,
+    load_subtasks,
     load_tasks,
     load_tasks_high_level,
     update_chunk_file_indices,
@@ -164,6 +165,7 @@ class LeRobotDatasetMetadata:
         check_version_compatibility(self.repo_id, self._version, CODEBASE_VERSION)
         self.tasks = load_tasks(self.root)
         self.tasks_high_level = load_tasks_high_level(self.root)
+        self.subtasks = load_subtasks(self.root)
         self.episodes = load_episodes(self.root)
         self.stats = load_stats(self.root)
 
@@ -520,6 +522,8 @@ class LeRobotDatasetMetadata:
         _validate_feature_names(features)
 
         obj.tasks = None
+        obj.tasks_high_level = None
+        obj.subtasks = None
         obj.episodes = None
         obj.stats = None
         obj.info = create_empty_dataset_info(
@@ -1068,6 +1072,12 @@ class LeRobotDataset(torch.utils.data.Dataset):
             high_level_task_idx = item["task_index_high_level"].item()
             item["robot_utterance"] = self.meta.tasks_high_level.iloc[high_level_task_idx]["robot_utterance"]
             item["user_prompt"] = self.meta.tasks_high_level.iloc[high_level_task_idx]["user_prompt"]
+        
+        # optionally add subtask information
+        if "subtask_index" in self.features and self.meta.subtasks is not None:
+            subtask_idx = item["subtask_index"].item()
+            item["subtask"] = self.meta.subtasks.iloc[subtask_idx].name
+        
         return item
 
     def __repr__(self):

src/lerobot/datasets/utils.py

@@ -62,6 +62,7 @@ CHUNK_FILE_PATTERN = "chunk-{chunk_index:03d}/file-{file_index:03d}"
 DEFAULT_TASKS_PATH = "meta/tasks.parquet"
 DEFAULT_EPISODES_PATH = EPISODES_DIR + "/" + CHUNK_FILE_PATTERN + ".parquet"
 DEFAULT_TASKS_HIGH_LEVEL_PATH = "meta/tasks_high_level.parquet"
+DEFAULT_SUBTASKS_PATH = "meta/subtasks.parquet"
 DEFAULT_DATA_PATH = DATA_DIR + "/" + CHUNK_FILE_PATTERN + ".parquet"
 DEFAULT_VIDEO_PATH = VIDEO_DIR + "/{video_key}/" + CHUNK_FILE_PATTERN + ".mp4"
 DEFAULT_IMAGE_PATH = "images/{image_key}/episode-{episode_index:06d}/frame-{frame_index:06d}.png"
@@ -353,9 +354,20 @@ def load_tasks(local_dir: Path) -> pandas.DataFrame:
     tasks = pd.read_parquet(local_dir / DEFAULT_TASKS_PATH)
     return tasks
 
-def load_tasks_high_level(local_dir: Path) -> pandas.DataFrame:
+def load_tasks_high_level(local_dir: Path) -> pandas.DataFrame | None:
-    tasks = pd.read_parquet(local_dir / DEFAULT_TASKS_HIGH_LEVEL_PATH)
+    """Load high-level tasks from tasks_high_level.parquet if it exists."""
-    return tasks
+    tasks_high_level_path = local_dir / DEFAULT_TASKS_HIGH_LEVEL_PATH
+    if tasks_high_level_path.exists():
+        return pd.read_parquet(tasks_high_level_path)
+    return None
+
+
+def load_subtasks(local_dir: Path) -> pandas.DataFrame | None:
+    """Load subtasks from subtasks.parquet if it exists."""
+    subtasks_path = local_dir / DEFAULT_SUBTASKS_PATH
+    if subtasks_path.exists():
+        return pd.read_parquet(subtasks_path)
+    return None
 
 def write_episodes(episodes: Dataset, local_dir: Path) -> None:
     """Write episode metadata to a parquet file in the LeRobot v3.0 format.

src/lerobot/policies/pi05_full/annotate/run_pgen.sh

@@ -9,21 +9,28 @@ MODEL="Qwen/Qwen3-VL-30B-A3B-Instruct"
 # or: MODEL="Qwen/Qwen2-VL-7B-Instruct"
 
 
-OUTPUT_DIR="/fsx/jade_choghari/outputs/collect-data-pgen"
+OUTPUT_DIR="/fsx/jade_choghari/outputs/collect-data-pgen_new"
+
 BATCH_SIZE=32
 TEMPERATURE=0.9
 SAMPLE_INTERVAL=5.0  # generate dialogue every 1 second (all episodes processed)
 
-# run synthetic data generation (all episodes processed)
+# Run subtask annotation
-python examples/dataset/annotate_pgen.py \
+python /admin/home/jade_choghari/lerobot/src/lerobot/policies/pi05_full/annotate/subtask_annotate.py \
     --repo-id "$REPO_ID" \
-    --model "$MODEL" \
+    --video-key observation.images.base \
     --output-dir "$OUTPUT_DIR" \
-    --temperature "$TEMPERATURE" \
+    --output-repo-id "jadechoghari/collect-data-with-subtasks"
-    --batch-size "$BATCH_SIZE" \
+# run synthetic data generation (all episodes processed)
-    --sample-interval "$SAMPLE_INTERVAL" \
+# python examples/dataset/annotate_pgen.py \
-    --image-key observation.images.base \
+#     --repo-id "$REPO_ID" \
-    --num-image-views-per-sample 1
+#     --model "$MODEL" \
+#     --output-dir "$OUTPUT_DIR" \
+#     --temperature "$TEMPERATURE" \
+#     --batch-size "$BATCH_SIZE" \
+#     --sample-interval "$SAMPLE_INTERVAL" \
+#     --image-key observation.images.base \
+#     --num-image-views-per-sample 1
 
 # for faster testing, increase sample interval:
 # --sample-interval 5.0  # Samples every 5 seconds (much faster)

src/lerobot/policies/pi05_full/annotate/subtask_annotate.py

@@ -19,13 +19,17 @@ Automatic Skill Annotation for LeRobot Datasets.
 
 This script performs automatic subtask/skill labeling for ANY LeRobot dataset using
 Vision-Language Models (VLMs). It segments each robot demonstration into short atomic
-skills (1-3 seconds each) and updates the dataset's task field.
+skills (1-3 seconds each) and creates a new dataset with subtask annotations.
 
 The pipeline:
 1. Loads a LeRobot dataset (local or from HuggingFace Hub)
 2. For each episode, extracts video frames
 3. Uses a VLM to identify skill boundaries and labels
-4. Updates the dataset's task metadata with skill annotations
+4. Creates a subtasks.parquet file with unique subtasks
+5. Adds a subtask_index feature to the dataset
+
+NOTE: This script does NOT modify the original tasks.parquet file. It creates a
+separate subtask hierarchy while preserving the original task annotations.
 
 Supported VLMs (modular design allows easy extension):
 - Qwen2-VL (default): "Qwen/Qwen2-VL-7B-Instruct"
@@ -37,6 +41,7 @@ python examples/dataset/annotate.py \
     --repo-id your-username/your-dataset \
     --video-key observation.images.base \
     --model Qwen/Qwen2-VL-7B-Instruct \
+    --output-dir /path/to/output \
     --push-to-hub
 ```
 
@@ -44,14 +49,16 @@ Or with a local dataset:
 ```bash
 python examples/dataset/annotate.py \
     --data-dir /path/to/local/dataset \
-    --video-key observation.images.base
+    --video-key observation.images.base \
+    --output-dir /path/to/output
 ```
-After running, you can access the skill for any frame via:
+
+After running, you can access the subtask for any frame via:
 ```python
-dataset = LeRobotDataset(repo_id="your/dataset")
+dataset = LeRobotDataset(repo_id="your/dataset_with_subtasks")
 item = dataset[100]
-task_idx = item["task_index"]
+subtask_idx = item["subtask_index"]
-skill_name = dataset.meta.tasks.iloc[task_idx.item()].name
+subtask_name = dataset.meta.subtasks.iloc[subtask_idx.item()].name
 ```
 """
 
@@ -66,10 +73,13 @@ from pathlib import Path
 from typing import Any
 
 import cv2
+import numpy as np
+import pandas as pd
 import torch
 from rich.console import Console
 from rich.progress import Progress, SpinnerColumn, TextColumn
 
+from lerobot.datasets.dataset_tools import add_features
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 
 
@@ -856,24 +866,18 @@ def get_skill_for_timestamp(skills: list[Skill], timestamp: float) -> Skill | No
     return skills[-1] if skills else None  # Fallback to last skill
 
 
-def update_dataset_tasks(
+def create_subtasks_dataframe(
-    dataset: LeRobotDataset,
     annotations: dict[int, EpisodeSkills],
-) -> dict[str, int]:
+) -> tuple[pd.DataFrame, dict[str, int]]:
     """
-    Register all unique skill names as new tasks in the dataset.
+    Create a subtasks DataFrame from skill annotations.
 
     Args:
-        dataset: The LeRobot dataset to update
         annotations: Dictionary of episode skills
 
     Returns:
-        Dictionary mapping skill name to task_index
+        Tuple of (subtasks_df, skill_to_subtask_idx mapping)
     """
-    import pandas as pd
-
-    from lerobot.datasets.utils import write_tasks
-
     console = Console()
 
     # Collect all unique skill names
@@ -882,163 +886,135 @@ def update_dataset_tasks(
         for skill in episode_skills.skills:
             all_skill_names.add(skill.name)
 
-    console.print(f"[cyan]Found {len(all_skill_names)} unique skills[/cyan]")
+    console.print(f"[cyan]Found {len(all_skill_names)} unique subtasks[/cyan]")
 
-    # Build new tasks DataFrame
+    # Build subtasks DataFrame
-    # Start with existing tasks if any
+    subtask_data = []
-    if dataset.meta.tasks is not None and len(dataset.meta.tasks) > 0:
+    for i, skill_name in enumerate(sorted(all_skill_names)):
-        existing_tasks = set(dataset.meta.tasks.index.tolist())
+        subtask_data.append({
-        max_task_idx = dataset.meta.tasks["task_index"].max()
+            "subtask": skill_name,
-    else:
+            "subtask_index": i,
-        existing_tasks = set()
+        })
-        max_task_idx = -1
 
-    # Add new skills as tasks
+    subtasks_df = pd.DataFrame(subtask_data).set_index("subtask")
-    new_tasks = all_skill_names - existing_tasks
-    if new_tasks:
-        new_task_data = []
-        for i, skill_name in enumerate(sorted(new_tasks)):
-            new_task_data.append({
-                "task": skill_name,
-                "task_index": max_task_idx + 1 + i,
-            })
 
-        new_tasks_df = pd.DataFrame(new_task_data).set_index("task")
+    # Build skill name to subtask_index mapping
-
+    skill_to_subtask_idx = {
-        if dataset.meta.tasks is not None and len(dataset.meta.tasks) > 0:
+        skill_name: int(subtasks_df.loc[skill_name, "subtask_index"])
-            dataset.meta.tasks = pd.concat([dataset.meta.tasks, new_tasks_df])
+        for skill_name in all_skill_names
-        else:
-            dataset.meta.tasks = new_tasks_df
-
-        # Write updated tasks to disk
-        write_tasks(dataset.meta.tasks, dataset.root)
-        console.print(f"[green]✓ Added {len(new_tasks)} new tasks to tasks.parquet[/green]")
-
-    # Build skill name to task_index mapping
-    skill_to_task_idx = {
-        task_name: int(dataset.meta.tasks.loc[task_name, "task_index"])
-        for task_name in all_skill_names
     }
 
-    return skill_to_task_idx
+    return subtasks_df, skill_to_subtask_idx
 
 
-def update_frame_task_indices(
+def save_subtasks(
+    subtasks_df: pd.DataFrame,
+    dataset_root: Path,
+    console: Console | None = None,
+) -> None:
+    """Save subtasks to subtasks.parquet."""
+    if console is None:
+        console = Console()
+    
+    output_path = dataset_root / "meta" / "subtasks.parquet"
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    
+    subtasks_df.to_parquet(output_path, engine="pyarrow", compression="snappy")
+    console.print(f"[green]✓ Saved subtasks to {output_path}[/green]")
+
+
+def create_subtask_index_array(
     dataset: LeRobotDataset,
     annotations: dict[int, EpisodeSkills],
-    skill_to_task_idx: dict[str, int],
+    skill_to_subtask_idx: dict[str, int],
-) -> None:
+) -> np.ndarray:
     """
-    Update the task_index for each frame based on skill annotations.
+    Create a subtask_index array for each frame based on skill annotations.
-
-    This reads the data parquet files, updates task_index based on which
-    skill covers each frame's timestamp, and writes back to disk.
 
     Args:
-        dataset: The LeRobot dataset to update
+        dataset: The LeRobot dataset
         annotations: Dictionary of episode skills
-        skill_to_task_idx: Mapping from skill name to task_index
+        skill_to_subtask_idx: Mapping from skill name to subtask_index
-    """
-    import pandas as pd
 
+    Returns:
+        Array of subtask indices for each frame in the dataset
+    """
     console = Console()
 
-    # Group episodes by their data file (chunk_index, file_index)
+    # Array to store subtask index for each frame
-    episodes_by_file: dict[tuple[int, int], list[int]] = {}
+    full_dataset_length = len(dataset)
-    for ep_idx in annotations.keys():
+    subtask_indices = np.zeros(full_dataset_length, dtype=np.int64)
+
+    console.print(f"[cyan]Creating subtask_index array for {full_dataset_length} frames...[/cyan]")
+
+    # Assign subtask_index for each annotated episode
+    for ep_idx, episode_skills in annotations.items():
+        skills = episode_skills.skills
+
+        # Get episode frame range
         ep = dataset.meta.episodes[ep_idx]
-        chunk_idx = ep["data/chunk_index"]
+        ep_from = ep["dataset_from_index"]
-        file_idx = ep["data/file_index"]
+        ep_to = ep["dataset_to_index"]
-        key = (chunk_idx, file_idx)
-        if key not in episodes_by_file:
-            episodes_by_file[key] = []
-        episodes_by_file[key].append(ep_idx)
 
-    # Process each data file
+        # Process each frame in the episode
-    for (chunk_idx, file_idx), episode_indices in episodes_by_file.items():
+        for frame_idx in range(ep_from, ep_to):
-        data_path = dataset.root / dataset.meta.data_path.format(
+            frame = dataset[frame_idx]
-            chunk_index=chunk_idx, file_index=file_idx
+            timestamp = frame["timestamp"].item()
-        )
             
-        if not data_path.exists():
+            # Find which skill covers this timestamp
-            console.print(f"[yellow]Warning: Data file not found: {data_path}[/yellow]")
+            skill = get_skill_for_timestamp(skills, timestamp)
-            continue
 
-        # Read the parquet file
+            if skill and skill.name in skill_to_subtask_idx:
-        df = pd.read_parquet(data_path)
+                subtask_idx = skill_to_subtask_idx[skill.name]
-        original_task_indices = df["task_index"].copy()
+                subtask_indices[frame_idx] = subtask_idx
-        updated_count = 0
 
-        # Update task_index for each episode in this file
+    console.print(f"[green]✓ Created subtask_index array[/green]")
-        for ep_idx in episode_indices:
+    return subtask_indices
-            if ep_idx not in annotations:
-                continue
-
-            episode_skills = annotations[ep_idx]
-            skills = episode_skills.skills
-
-            # Get episode frame range
-            ep = dataset.meta.episodes[ep_idx]
-            ep_from = ep["dataset_from_index"]
-            ep_to = ep["dataset_to_index"]
-
-            # Filter to rows for this episode
-            episode_mask = (df["index"] >= ep_from) & (df["index"] < ep_to)
-            episode_rows = df.loc[episode_mask]
-
-            # Update task_index for each frame based on its timestamp
-            for idx, row in episode_rows.iterrows():
-                timestamp = row["timestamp"]
-                skill = get_skill_for_timestamp(skills, timestamp)
-
-                if skill and skill.name in skill_to_task_idx:
-                    new_task_idx = skill_to_task_idx[skill.name]
-                    if df.at[idx, "task_index"] != new_task_idx:
-                        df.at[idx, "task_index"] = new_task_idx
-                        updated_count += 1
-
-        # Write back if any changes were made
-        if updated_count > 0:
-            df.to_parquet(data_path, engine="pyarrow", compression="snappy", index=False)
-            console.print(
-                f"[green]✓ Updated {updated_count} frame task_indices in {data_path.name}[/green]"
-            )
 
 
 def save_skill_annotations(
     dataset: LeRobotDataset,
     annotations: dict[int, EpisodeSkills],
-    output_path: Path | None = None,
+    output_dir: Path | None = None,
-) -> None:
+    repo_id: str | None = None,
+) -> LeRobotDataset:
     """
-    Save skill annotations to the dataset, updating both:
+    Save skill annotations to the dataset by:
-    1. The tasks.parquet with new skill names
+    1. Creating a subtasks.parquet file with unique subtasks
-    2. The per-frame task_index in data parquet files
+    2. Adding a subtask_index feature to the dataset
+    3. Saving raw skill annotations as JSON for reference
 
-    This function updates the task field for each frame based on
+    This function does NOT modify tasks.parquet - it keeps the original tasks intact
-    which skill covers that frame's timestamp.
+    and creates a separate subtask hierarchy.
 
     Args:
-        dataset: The LeRobot dataset to update
+        dataset: The LeRobot dataset to annotate
         annotations: Dictionary of episode skills
-        output_path: Optional custom output path for the annotations JSON
+        output_dir: Optional directory to save the modified dataset
+        repo_id: Optional repository ID for the new dataset
+
+    Returns:
+        New dataset with subtask_index feature added
     """
     console = Console()
 
     if not annotations:
         console.print("[yellow]No annotations to save[/yellow]")
-        return
+        return dataset
 
-    # Step 1: Register all unique skills as tasks
+    # Step 1: Create subtasks DataFrame
-    console.print("[cyan]Registering skills as tasks...[/cyan]")
+    console.print("[cyan]Creating subtasks DataFrame...[/cyan]")
-    skill_to_task_idx = update_dataset_tasks(dataset, annotations)
+    subtasks_df, skill_to_subtask_idx = create_subtasks_dataframe(annotations)
     
-    # Step 2: Update per-frame task_index in data parquet files
+    # Step 2: Create subtask_index array for all frames
-    console.print("[cyan]Updating per-frame task indices...[/cyan]")
+    console.print("[cyan]Creating subtask_index array...[/cyan]")
-    update_frame_task_indices(dataset, annotations, skill_to_task_idx)
+    subtask_indices = create_subtask_index_array(dataset, annotations, skill_to_subtask_idx)
 
-    # Step 3: Also save the raw skill annotations as JSON for reference
+    # Step 3: Save subtasks.parquet to the original dataset root
-    skills_path = output_path or (dataset.root / "meta" / "skills.json")
+    save_subtasks(subtasks_df, dataset.root, console)
+
+    # Step 4: Save the raw skill annotations as JSON for reference
+    skills_path = dataset.root / "meta" / "skills.json"
     skills_path.parent.mkdir(parents=True, exist_ok=True)
     
     # Load existing skills data if it exists and is not empty
@@ -1062,16 +1038,16 @@ def save_skill_annotations(
         merged_episodes = existing_skills_data.get("episodes", {}).copy()
         merged_episodes.update(new_episodes)
         
-        # Merge skill_to_task_index mappings
+        # Merge skill_to_subtask_index mappings
-        merged_skill_to_task = existing_skills_data.get("skill_to_task_index", {}).copy()
+        merged_skill_to_subtask = existing_skills_data.get("skill_to_subtask_index", {}).copy()
-        merged_skill_to_task.update(skill_to_task_idx)
+        merged_skill_to_subtask.update(skill_to_subtask_idx)
         
         # Use existing coarse_description if available, otherwise use new one
         coarse_desc = existing_skills_data.get("coarse_description", annotations[next(iter(annotations))].description)
         
         skills_data = {
             "coarse_description": coarse_desc,
-            "skill_to_task_index": merged_skill_to_task,
+            "skill_to_subtask_index": merged_skill_to_subtask,
             "episodes": merged_episodes,
         }
         console.print(f"[cyan]Updated {len(new_episodes)} episode(s), total episodes in skills.json: {len(merged_episodes)}[/cyan]")
@@ -1079,7 +1055,7 @@ def save_skill_annotations(
         # No existing data, create new
         skills_data = {
             "coarse_description": annotations[next(iter(annotations))].description,
-            "skill_to_task_index": skill_to_task_idx,
+            "skill_to_subtask_index": skill_to_subtask_idx,
             "episodes": new_episodes,
         }
 
@@ -1088,8 +1064,49 @@ def save_skill_annotations(
 
     console.print(f"[green]✓ Saved skill annotations to {skills_path}[/green]")
 
-    # Reload the dataset's hf_dataset to reflect changes
+    # Step 5: Add subtask_index feature to dataset using add_features
-    dataset._lazy_loading = True
+    console.print("[cyan]Adding subtask_index feature to dataset...[/cyan]")
+    
+    # Determine output directory and repo_id
+    if output_dir is None:
+        output_dir = dataset.root.parent / f"{dataset.root.name}_with_subtasks"
+    else:
+        output_dir = Path(output_dir)
+    
+    if repo_id is None:
+        repo_id = f"{dataset.repo_id}_with_subtasks"
+    
+    # Add feature using dataset_tools
+    feature_info = {
+        "dtype": "int64",
+        "shape": (1,),
+        "names": None,
+    }
+    new_dataset = add_features(
+        dataset=dataset,
+        features={
+            "subtask_index": (subtask_indices, feature_info),
+        },
+        output_dir=output_dir,
+        repo_id=repo_id,
+    )
+    
+    # Copy subtasks.parquet to new output directory
+    import shutil
+    shutil.copy(
+        dataset.root / "meta" / "subtasks.parquet",
+        output_dir / "meta" / "subtasks.parquet"
+    )
+    shutil.copy(
+        dataset.root / "meta" / "skills.json",
+        output_dir / "meta" / "skills.json"
+    )
+    
+    console.print(f"[bold green]✓ Successfully added subtask_index feature![/bold green]")
+    console.print(f"  New dataset saved to: {new_dataset.root}")
+    console.print(f"  Total subtasks: {len(subtasks_df)}")
+    
+    return new_dataset
 
 
 def load_skill_annotations(dataset_root: Path) -> dict | None:
@@ -1112,17 +1129,20 @@ def main():
         epilog=textwrap.dedent("""\
             Examples:
               # Annotate a HuggingFace Hub dataset
-              python annotate.py --repo-id user/dataset --video-key observation.images.base
+              python annotate.py --repo-id user/dataset --video-key observation.images.base \\
+                  --output-dir ./output
 
               # Annotate a local dataset with custom batch size
-              python annotate.py --data-dir /path/to/dataset --video-key observation.images.base --batch-size 16
+              python annotate.py --data-dir /path/to/dataset --video-key observation.images.base \\
+                  --batch-size 16 --output-dir ./output
 
               # Use a specific model
               python annotate.py --repo-id user/dataset --video-key observation.images.base \\
-                  --model Qwen/Qwen2-VL-7B-Instruct
+                  --model Qwen/Qwen2-VL-7B-Instruct --output-dir ./output
 
               # Push annotated dataset to Hub
-              python annotate.py --repo-id user/dataset --video-key observation.images.base --push-to-hub
+              python annotate.py --repo-id user/dataset --video-key observation.images.base \\
+                  --output-dir ./output --push-to-hub
         """),
     )
 
@@ -1186,9 +1206,14 @@ def main():
         help="Push annotated dataset to HuggingFace Hub",
     )
     parser.add_argument(
-        "--output-path",
+        "--output-dir",
         type=str,
-        help="Custom output path for annotations JSON",
+        help="Output directory for modified dataset with subtask_index feature",
+    )
+    parser.add_argument(
+        "--output-repo-id",
+        type=str,
+        help="Repository ID for the new dataset (default: original_repo_id_with_subtasks)",
     )
 
     args = parser.parse_args()
@@ -1232,14 +1257,16 @@ def main():
     )
 
     # Save annotations
-    output_path = Path(args.output_path) if args.output_path else None
+    output_dir = Path(args.output_dir) if args.output_dir else None
-    save_skill_annotations(dataset, annotations, output_path)
+    output_repo_id = args.output_repo_id if args.output_repo_id else None
+    new_dataset = save_skill_annotations(dataset, annotations, output_dir, output_repo_id)
 
     # Summary
     total_skills = sum(len(ann.skills) for ann in annotations.values())
     console.print(f"\n[bold green]✓ Annotation complete![/bold green]")
     console.print(f"  Episodes annotated: {len(annotations)}")
-    console.print(f"  Total skills identified: {total_skills}")
+    console.print(f"  Total subtasks identified: {total_skills}")
+    console.print(f"  Dataset with subtask_index saved to: {new_dataset.root}")
 
     # Push to hub if requested
     if args.push_to_hub:
@@ -1248,8 +1275,8 @@ def main():
         else:
             console.print("[cyan]Pushing to HuggingFace Hub...[/cyan]")
             try:
-                dataset.push_to_hub(push_videos=False)
+                new_dataset.push_to_hub(push_videos=False)
-                console.print(f"[green]✓ Pushed to {args.repo_id}[/green]")
+                console.print(f"[green]✓ Pushed to {output_repo_id or f'{args.repo_id}_with_subtasks'}[/green]")
             except Exception as e:
                 console.print(f"[red]Push failed: {e}[/red]")