README.md

---
license: mit
pipeline_tag: robotics
library_name: lerobot
tags:
  - action-chunking-with-transformers
---

# Robot Learning: A Tutorial - ACT Model

This repository contains a model checkpoint (`act-resnet18-upside-down-side-v0.1`) associated with the paper "[Robot Learning: A Tutorial](https://huggingface.co/papers/2510.12403)". This tutorial navigates the landscape of modern robot learning, providing practical examples using the `lerobot` library developed by Hugging Face. The model provided here is an instance of the Action Chunking with Transformers (ACT) framework.

-   **Paper:** [Robot Learning: A Tutorial](https://huggingface.co/papers/2510.12403)
-   **GitHub Repository:** [https://github.com/fracapuano/robot-learning-tutorial](https://github.com/fracapuano/robot-learning-tutorial)
-   **Project Page (Hugging Face Space):** [https://huggingface.co/spaces/lerobot/robot-learning-tutorial](https://huggingface.co/spaces/lerobot/robot-learning-tutorial)

## Abstract
Robot learning is at an inflection point, driven by rapid advancements in machine learning and the growing availability of large-scale robotics data. This shift from classical, model-based methods to data-driven, learning-based paradigms is unlocking unprecedented capabilities in autonomous systems. This tutorial navigates the landscape of modern robot learning, charting a course from the foundational principles of Reinforcement Learning and Behavioral Cloning to generalist, language-conditioned models capable of operating across diverse tasks and even robot embodiments. This work is intended as a guide for researchers and practitioners, and our goal is to equip the reader with the conceptual understanding and practical tools necessary to contribute to developments in robot learning, with ready-to-use examples implemented in $\texttt{lerobot}$.

## Model Details
This model checkpoint is configured as an Action Chunking with Transformers (ACT) policy, as defined in its `config.json`. It utilizes a `resnet18` vision backbone for processing visual observations and is designed for robotic action prediction.

-   **Model Type:** `act` (Action Chunking with Transformers)
-   **Vision Backbone:** `resnet18`
-   **Input Features:**
    -   `observation.state`: State vector of shape `[6]`
    -   `observation.images.up`: Visual input from an 'up' camera, shape `[3, 480, 640]`
    -   `observation.images.side`: Visual input from a 'side' camera, shape `[3, 480, 640]`
-   **Output Features:**
    -   `action`: Action vector of shape `[6]`
-   **Uses VAE:** `true`

## Usage
For detailed instructions on installation, training, and utilizing robot learning models within the `lerobot` ecosystem, please refer to the comprehensive [GitHub repository](https://github.com/fracapuano/robot-learning-tutorial) of the "Robot Learning: A Tutorial". The repository provides various code examples and guidance for implementing and experimenting with such models.

## License
The code examples within the "Robot Learning: A Tutorial" GitHub repository are licensed under the [MIT License](https://opensource.org/licenses/MIT). This model artifact is associated with that codebase.