Documentation Index Fetch the complete documentation index at: https://mintlify.com/huggingface/lerobot/llms.txt
Use this file to discover all available pages before exploring further.
LeRobot provides interactive Jupyter notebooks to help you learn robot learning concepts and experiment with the library in a hands-on way.
Getting Started with Notebooks Installation To use Jupyter notebooks with LeRobot, install the notebook dependencies:
pip install jupyter notebook # or if using conda conda install jupyter notebook
Launching Jupyter From your LeRobot environment:
This will open a browser window where you can create and run notebooks.
Available Notebooks While LeRobot focuses on Python scripts for reproducibility, you can easily convert any example into a notebook.
Creating Your Own Notebooks You can create notebooks for any LeRobot workflow:
Dataset Exploration
Load and visualize datasets interactively: from lerobot.datasets.lerobot_dataset import LeRobotDataset import matplotlib.pyplot as plt # Load dataset dataset = LeRobotDataset( "lerobot/aloha_mobile_cabinet" ) # Visualize frames frame = dataset[ 0 ] plt.imshow(frame[ 'observation.images.top' ]) plt.show()
Policy Training
Train policies with interactive progress tracking: from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy from tqdm.notebook import tqdm # Training loop with notebook-friendly progress bar for step in tqdm( range (training_steps)): loss = train_step(policy, batch) # Log metrics
Visualization
Create interactive visualizations of robot trajectories and policies: import plotly.graph_objects as go # Plot robot trajectories fig = go.Figure( data = go.Scatter3d( x = positions[:, 0 ], y = positions[:, 1 ], z = positions[:, 2 ], mode = 'lines' )) fig.show()
Interactive Learning Resources
Robot Learning Tutorial A free, hands-on course to learn robot learning using LeRobot
Hugging Face Datasets Learn about the datasets library used by LeRobot
PyTorch Tutorials Deepen your understanding of PyTorch, the foundation of LeRobot
Visualize on Hub Explore and visualize datasets directly on the Hugging Face Hub
Common Notebook Patterns Pattern 1: Dataset Inspection from lerobot.datasets.lerobot_dataset import LeRobotDataset from pprint import pprint # Load dataset dataset = LeRobotDataset( "lerobot/pusht" ) # Inspect metadata print ( f "Total frames: { len (dataset) } " ) print ( f "Number of episodes: { dataset.num_episodes } " ) print ( f " \n Dataset info:" ) pprint(dataset.meta.info) # Visualize statistics import pandas as pd stats_df = pd.DataFrame(dataset.meta.stats) stats_df.plot( kind = 'bar' , title = 'Dataset Statistics' )
Pattern 2: Policy Inference from lerobot.policies.factory import make_policy import torch # Load pretrained policy policy = make_policy( pretrained = "lerobot/act_aloha_sim_transfer_cube_human" ) # Run inference with torch.no_grad(): observation = get_observation() # Your observation action = policy.select_action(observation) print ( f "Predicted action: { action } " )
Pattern 3: Training Visualization from IPython.display import clear_output import matplotlib.pyplot as plt losses = [] for step in range (num_steps): loss = train_step(policy, batch) losses.append(loss) # Update plot every 10 steps if step % 10 == 0 : clear_output( wait = True ) plt.plot(losses) plt.xlabel( 'Step' ) plt.ylabel( 'Loss' ) plt.title( 'Training Progress' ) plt.show()
Notebook Best Practices
Use Cell Magic for Timing
Measure execution time of cells: %% time dataset = LeRobotDataset( "lerobot/aloha_mobile_cabinet" )
Automatically reload modules when they change: % load_ext autoreload % autoreload 2
Save figures and models periodically: # Save figure plt.savefig( 'training_curve.png' ) # Save model checkpoint torch.save(policy.state_dict(), 'checkpoint.pt' )
Clear GPU memory when needed: import torch torch.cuda.empty_cache() # Or delete large objects del large_dataset import gc gc.collect()
Converting Scripts to Notebooks You can convert any LeRobot example script to a notebook:
# Install jupytext pip install jupytext # Convert Python script to notebook jupytext --to notebook examples/dataset/load_lerobot_dataset.py
This creates a .ipynb file that you can open in Jupyter.
Run LeRobot notebooks on cloud platforms:
Google Colab: Free GPU access for experimentationKaggle Notebooks: Free TPU/GPU resourcesAmazon SageMaker: Scalable cloud notebooksPaperspace Gradient: GPU-powered notebooks
Example Colab Setup # Install LeRobot in Colab ! pip install lerobot # Import and verify import lerobot print ( f "LeRobot version: { lerobot. __version__ } " )
Sharing Your Notebooks
Clean Your Notebook
Remove outputs and sensitive data before sharing
Add Documentation
Include markdown cells explaining your approach
Share on Hub
Upload to Hugging Face Spaces or GitHub
Share your notebooks with the community on Discord or GitHub Discussions !