Understanding the Target Audience
The primary audience for this tutorial includes data scientists, machine learning engineers, and robotics developers eager to implement behavior cloning policies in their robotic systems. These professionals often face challenges such as the complexity of setting up machine learning environments, ensuring reproducibility in experiments, and efficiently training models on high-dimensional datasets.
They aim to master contemporary libraries like LeRobot, deepen their understanding of end-to-end robotics learning, and apply their knowledge in real-world scenarios. Clear, concise tutorials with a step-by-step approach, well-documented code snippets, and visual outputs are highly valued in this community.
Tutorial Overview
This tutorial serves as a comprehensive guide for using Hugging Face’s LeRobot library to train and evaluate a behavior-cloning policy on the PushT dataset. We will start by setting up the environment in Google Colab and installing the necessary dependencies.
Setting Up Your Environment
To kick things off, we need to install the required 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 will load the PushT dataset using the LeRobot library and inspect its structure. This step involves identifying keys corresponding to images, states, and actions, ensuring 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 a flattened state and action. This process includes 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
In this section, we will define a compact visuomotor policy that utilizes a convolutional neural network (CNN) backbone to extract 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 involves 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. These visualizations will be saved for review.
Visualization Code
frames = []
...
imageio.mimsave(video_path, frames, fps=10)
Conclusion
This tutorial demonstrates 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 a practical entry into robot learning without the need for physical hardware.
We are now ready to extend our learning by exploring advanced models and datasets, as well as sharing our trained policies. For further information, feel free to check out our GitHub Page for Tutorials, Codes, and Notebooks.
FAQ
- What is behavior cloning in robotics? Behavior cloning is a technique where a model learns to imitate the actions of a human or another agent by observing their behavior.
- How does LeRobot simplify the training process? LeRobot provides a unified framework for data handling, model definition, and evaluation, making it easier to implement behavior cloning policies.
- What are the advantages of using Google Colab for this tutorial? Google Colab offers free access to powerful GPU resources, making it ideal for training machine learning models without the need for local hardware.
- Can I use my own dataset with LeRobot? Yes, LeRobot allows you to load custom datasets, provided they are formatted correctly.
- What should I do if my model isn’t performing well? Consider adjusting hyperparameters, increasing the amount of training data, or refining the model architecture to improve performance.
