Add model card for Robot Learning tutorial model

This PR adds a comprehensive model card for the model associated with the "Robot Learning: A Tutorial" paper.

The update includes:
- Linking the model to its scientific publication: [Robot Learning: A Tutorial](https://huggingface.co/papers/2510.12403).
- Adding the `robotics` pipeline tag to improve discoverability.
- Specifying `lerobot` as the `library_name`, consistent with the tutorial's implementation.
- Adding `action-chunking-with-transformers` as an additional tag, reflecting the model's type identified in `config.json`.
- Providing links to the GitHub repository and the Hugging Face Space (project page).
- Including the paper's abstract and detailed model configuration from `config.json`.
- Setting the license to MIT, based on the code examples provided in the tutorial's GitHub.

Please review and merge if everything looks good.

README.md

@@ -0,0 +1,37 @@
+---
+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.