Dataset Manipulation Tools

Tools for editing, splitting, and merging LeRobot datasets

LeRobot provides utilities for manipulating datasets: deleting episodes, splitting datasets, merging multiple datasets, and modifying features.

Deleting Episodes

Remove unwanted episodes from a dataset:

from lerobot.datasets.lerobot_dataset import LeRobotDataset
from lerobot.datasets.dataset_tools import delete_episodes

# Load dataset
dataset = LeRobotDataset("lerobot/aloha_mobile_cabinet")
print(f"Original: {dataset.num_episodes} episodes")

# Delete specific episodes (e.g., bad demonstrations)
episodes_to_delete = [5, 12, 23, 45]

new_dataset = delete_episodes(
    dataset,
    episode_indices=episodes_to_delete,
    output_dir="./data/cleaned",
    repo_id="username/aloha_mobile_cabinet_cleaned"
)

print(f"Cleaned: {new_dataset.num_episodes} episodes")
print(f"Saved to: {new_dataset.root}")

How It Works

delete_episodes creates a new dataset by:

1. Copying data files, filtering out deleted episodes
2. Re-encoding video files to remove deleted episodes
3. Updating episode metadata with new indices
4. Recalculating dataset statistics

Splitting Datasets

Split a dataset into train/val/test splits:

By Fraction

from lerobot.datasets.dataset_tools import split_dataset

dataset = LeRobotDataset("lerobot/pusht")

# Split into train (80%), val (10%), test (10%)
splits = {
    "train": 0.8,
    "val": 0.1,
    "test": 0.1,
}

split_datasets = split_dataset(
    dataset,
    splits=splits,
    output_dir="./data/pusht_splits"
)

# Access individual splits
train_ds = split_datasets["train"]
val_ds = split_datasets["val"]
test_ds = split_datasets["test"]

print(f"Train: {train_ds.num_episodes} episodes")
print(f"Val: {val_ds.num_episodes} episodes")
print(f"Test: {test_ds.num_episodes} episodes")

By Episode Indices

# Manually specify which episodes go in each split
splits = {
    "train": [0, 1, 2, 3, 4, 10, 11, 12, 13, 14],
    "val": [5, 6, 15, 16],
    "test": [7, 8, 9, 17, 18, 19],
}

split_datasets = split_dataset(
    dataset,
    splits=splits,
    output_dir="./data/pusht_splits"
)

Use Cases

# Create splits for training
train_ds = split_datasets["train"]
val_ds = split_datasets["val"]

from torch.utils.data import DataLoader

train_loader = DataLoader(train_ds, batch_size=32, shuffle=True)
val_loader = DataLoader(val_ds, batch_size=32, shuffle=False)

for epoch in range(num_epochs):
    # Training
    for batch in train_loader:
        loss = train_step(batch)
    
    # Validation
    for batch in val_loader:
        val_loss = val_step(batch)

Merging Datasets

Combine multiple datasets into one:

from lerobot.datasets.dataset_tools import merge_datasets

# Load multiple datasets
dataset1 = LeRobotDataset("lerobot/pusht")
dataset2 = LeRobotDataset("lerobot/pusht_image")
dataset3 = LeRobotDataset("username/pusht_custom")

# Merge them
merged = merge_datasets(
    datasets=[dataset1, dataset2, dataset3],
    output_repo_id="username/pusht_merged",
    output_dir="./data/merged"
)

print(f"Merged dataset: {merged.num_episodes} episodes")
print(f"Total frames: {merged.num_frames}")

Requirements

Datasets must have:

• Same FPS
• Compatible features (same keys, dtypes, shapes)
• Same robot type (recommended)

Adding Features

Add new features to an existing dataset:

from lerobot.datasets.dataset_tools import add_features
import numpy as np

dataset = LeRobotDataset("lerobot/pusht")

# Compute new features (e.g., embeddings)
task_embeddings = np.random.randn(dataset.num_frames, 384).astype(np.float32)

# Define feature metadata
features_to_add = {
    "observation.task_embedding": (
        task_embeddings,
        {"dtype": "float32", "shape": [384], "names": None}
    )
}

# Add to dataset
new_dataset = add_features(
    dataset,
    features=features_to_add,
    output_dir="./data/pusht_with_embeddings",
    repo_id="username/pusht_with_embeddings"
)

# Check new feature
sample = new_dataset[0]
print(sample["observation.task_embedding"].shape)  # [384]

Adding Multiple Features

import torch
from transformers import AutoModel, AutoTokenizer

# Load models
text_model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")

# Compute embeddings for all frames
task_embeddings = []
for i in range(len(dataset)):
    sample = dataset[i]
    task = sample["task"]
    
    # Encode task
    inputs = tokenizer(task, return_tensors="pt", padding=True, truncation=True)
    with torch.no_grad():
        embedding = text_model(**inputs).last_hidden_state.mean(dim=1)
    task_embeddings.append(embedding.cpu().numpy())

task_embeddings = np.concatenate(task_embeddings, axis=0)

# Add multiple features at once
features_to_add = {
    "observation.task_embedding": (
        task_embeddings,
        {"dtype": "float32", "shape": [384], "names": None}
    ),
    "reward": (
        compute_rewards(dataset),  # Your reward function
        {"dtype": "float32", "shape": [1], "names": None}
    ),
}

new_dataset = add_features(
    dataset,
    features=features_to_add,
    output_dir="./data/pusht_augmented"
)

Using Callable for Dynamic Features

# Define a function to compute features per frame
def compute_velocity(frame_dict, episode_index, frame_index):
    """Compute velocity from consecutive states."""
    state = frame_dict["observation.state"]
    if frame_index == 0:
        return np.zeros_like(state)
    # Get previous state (simplified - you'd access from dataset)
    prev_state = get_previous_state(episode_index, frame_index - 1)
    velocity = (state - prev_state) * dataset.fps
    return velocity

# Add feature using callable
features_to_add = {
    "observation.velocity": (
        compute_velocity,  # Callable: (frame_dict, ep_idx, frame_idx) -> value
        {"dtype": "float32", "shape": [14], "names": None}
    )
}

new_dataset = add_features(dataset, features=features_to_add)

Removing Features

Remove features you don’t need:

from lerobot.datasets.dataset_tools import remove_feature

dataset = LeRobotDataset("lerobot/aloha_mobile_cabinet")

# Remove video features to create a lightweight dataset
features_to_remove = [
    "observation.images.top",
    "observation.images.wrist",
    "observation.images.front"
]

state_only_dataset = remove_feature(
    dataset,
    feature_names=features_to_remove,
    output_dir="./data/aloha_state_only",
    repo_id="username/aloha_mobile_cabinet_state_only"
)

print(f"Original features: {list(dataset.features.keys())}")
print(f"New features: {list(state_only_dataset.features.keys())}")

Restrictions

Cannot remove required features:

• timestamp
• frame_index
• episode_index
• index
• task_index

Modifying Features

Add and remove features in a single operation:

from lerobot.datasets.dataset_tools import modify_features

# Add embeddings and remove videos in one pass
new_dataset = modify_features(
    dataset,
    add_features={
        "observation.task_embedding": (
            task_embeddings,
            {"dtype": "float32", "shape": [384]}
        )
    },
    remove_features=["observation.images.top"],
    output_dir="./data/modified",
    repo_id="username/dataset_modified"
)

print(f"Modified dataset features: {list(new_dataset.features.keys())}")

Example Workflows

Clean and Split Dataset

from lerobot.datasets.lerobot_dataset import LeRobotDataset
from lerobot.datasets.dataset_tools import delete_episodes, split_dataset

# 1. Load original dataset
dataset = LeRobotDataset("lerobot/aloha_mobile_cabinet")

# 2. Remove failed demonstrations
bad_episodes = [3, 7, 15, 22, 31]
cleaned = delete_episodes(
    dataset,
    episode_indices=bad_episodes,
    repo_id="username/aloha_cleaned"
)

# 3. Split into train/val/test
splits = split_dataset(
    cleaned,
    splits={"train": 0.8, "val": 0.1, "test": 0.1},
    output_dir="./data/aloha_splits"
)

print(f"Train: {splits['train'].num_episodes} episodes")
print(f"Val: {splits['val'].num_episodes} episodes")
print(f"Test: {splits['test'].num_episodes} episodes")

Merge and Augment

from lerobot.datasets.dataset_tools import merge_datasets, add_features

# 1. Merge multiple collection sessions
session1 = LeRobotDataset("username/session1")
session2 = LeRobotDataset("username/session2")
session3 = LeRobotDataset("username/session3")

merged = merge_datasets(
    [session1, session2, session3],
    output_repo_id="username/all_sessions"
)

# 2. Add computed features
rewards = compute_sparse_rewards(merged)
embeddings = compute_language_embeddings(merged)

augmented = add_features(
    merged,
    features={
        "reward": (rewards, {"dtype": "float32", "shape": [1]}),
        "observation.language": (embeddings, {"dtype": "float32", "shape": [512]}),
    },
    repo_id="username/all_sessions_augmented"
)

# 3. Push to Hub
augmented.push_to_hub()

Create Lightweight Version

from lerobot.datasets.dataset_tools import modify_features

# Load full dataset
full_dataset = LeRobotDataset("lerobot/aloha_mobile_cabinet")

# Create lightweight version for quick experiments
lightweight = modify_features(
    full_dataset,
    remove_features=[
        "observation.images.top",
        "observation.images.wrist",
        "observation.images.front",
    ],
    repo_id="username/aloha_lightweight"
)

print(f"Original size: {full_dataset.num_frames} frames with videos")
print(f"Lightweight size: {lightweight.num_frames} frames, state only")

# Much faster to download and iterate
lightweight.push_to_hub()

Performance Considerations

Efficient Video Handling

# For large datasets, video re-encoding can be slow
# Delete/split operations automatically:
# - Copy videos directly if all episodes in a file are kept
# - Re-encode only files with mixed kept/deleted episodes

import logging
logging.basicConfig(level=logging.INFO)

# You'll see which files are copied vs re-encoded
deleted = delete_episodes(dataset, [5, 10, 15])
# INFO: Copying video file (all episodes kept)
# INFO: Re-encoding video file (mixed episodes)

Memory Usage

# Tools process data in chunks to avoid loading entire dataset
# Safe for datasets with millions of frames

large_dataset = LeRobotDataset("username/very-large-dataset")

# This processes data file-by-file
split_datasets = split_dataset(
    large_dataset,
    splits={"train": 0.9, "val": 0.1}
)

See Also

• Using LeRobotDataset - Loading and using datasets
• Porting Datasets - Creating new datasets
• Dataset Overview - Dataset format and sharing