Understanding the Target Audience

The audience for this tutorial primarily includes data scientists, machine learning engineers, and robotics developers who are looking to implement behavior cloning policies within their robotic systems. Their pain points often revolve around the complexities of setting up machine learning environments, the need for reproducibility in experiments, and the challenge of efficiently training models on high-dimensional datasets.

Their goals include mastering the use of contemporary libraries like LeRobot, improving their understanding of end-to-end robotics learning, and achieving practical applications in real-world scenarios. They are typically interested in clear, concise tutorials that provide a step-by-step approach to problem-solving. Additionally, they prefer well-documented code snippets and visual outputs that help clarify the learning process.

Tutorial Overview

This tutorial provides a comprehensive guide to using Hugging Face’s LeRobot library for training and evaluating a behavior-cloning policy on the PushT dataset. We will begin by setting up the environment in Google Colab and installing the required dependencies.

Setting Up Your Environment

To get started, we will install the necessary libraries and configure our environment. This includes importing essential modules, fixing the random seed for reproducibility, and determining the device type (GPU or CPU) for efficient training.

Installation Code

            !pip -q install --upgrade lerobot torch torchvision timm imageio[ffmpeg]
        

Loading the PushT Dataset

Next, we load the PushT dataset using the LeRobot library and inspect its structure. We will identify keys corresponding to images, states, and actions for consistent access throughout our training pipeline.

Loading Code

            REPO_ID = "lerobot/pusht"
            ds = LeRobotDataset(REPO_ID)
            print("Dataset length:", len(ds))
        

Data Preparation

We will wrap each sample in the dataset to obtain a normalized 96×96 image and flattened state and action. This involves shuffling, splitting into training and validation sets, and creating efficient DataLoaders for batching and shuffling.

Data Preparation Code

            wrapped = PushTWrapper(ds)
            ...
            train_loader = DataLoader(train_ds, batch_size=BATCH, shuffle=True, num_workers=2, pin_memory=True)
        

Defining the Model

We will define a compact visuomotor policy that utilizes a convolutional neural network (CNN) backbone for extracting image features. These features will be combined with the robot’s state to predict 2-D actions.

Model Code

            class SmallBackbone(nn.Module):
                ...
            policy = BCPolicy().to(DEVICE)
        

Training the Policy

The training process includes defining the optimizer, setting up a learning rate schedule, and evaluating model performance on a validation set. The best model is saved based on validation loss.

Training Code

            for epoch in range(EPOCHS):
                ...
                val_mse = evaluate()
        

Visualizing Results

After training, we will visualize the policy’s behavior by overlaying predicted action arrows on the frames from the PushT dataset and saving these visualizations for review.

Visualization Code

            frames = []
            ...
            imageio.mimsave(video_path, frames, fps=10)
        

Conclusion

This tutorial highlights how LeRobot integrates data handling, policy definition, and evaluation into a unified framework. By training a lightweight policy and visualizing predicted actions, we confirm that the library facilitates practical entry into robot learning without requiring physical hardware.

We are now equipped to extend our learning by exploring advanced models and datasets, as well as sharing our trained policies. For more information, feel free to check out our GitHub Page for Tutorials, Codes, and Notebooks.