Improve model card: Add robotics pipeline tag, lerobot library, and tutorial links

README.md

@@ -1,21 +1,35 @@
+---
+license: apache-2.0
+pipeline_tag: robotics
+library_name: lerobot
+---
+
 # π₀ (Pi0)
 
 These weights directly come from the Pytorch conversion script of openpi and their `pi0_base` model.
 
 π₀ is a **Vision-Language-Action model for general robot control**, from Physical Intelligence. The LeRobot implementation is adapted from their open source [OpenPI](https://github.com/Physical-Intelligence/openpi) repository.
 
+---
+**Paper:** [Robot Learning: A Tutorial](https://huggingface.co/papers/2510.12403)
+**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 `lerobot`.
+**Project Page:** [https://huggingface.co/spaces/lerobot/robot-learning-tutorial](https://huggingface.co/spaces/lerobot/robot-learning-tutorial)
+**Code for Tutorial:** [https://github.com/fracapuano/robot-learning-tutorial](https://github.com/fracapuano/robot-learning-tutorial)
+**Original Repository (OpenPI):** [https://github.com/Physical-Intelligence/openpi](https://github.com/Physical-Intelligence/openpi)
+---
+
 ## Model Overview
 
-π₀ represents a breakthrough in robotics as the first general-purpose robot foundation model developed by [Physical Intelligence](https://www.physicalintelligence.company/blog/pi0). Unlike traditional robots that are narrow specialists programmed for repetitive motions, π₀ is designed to be a generalist policy that can understand visual inputs, interpret natural language instructions, and control a variety of different robots across diverse tasks.
+π₀ represents a breakthrough in robotics as the first general-purpose robot foundation model developed by [Physical Intelligence](https://www.physicalintelligence.company/blog/pi0). Unlike traditional robots that are narrow specialists programmed for repetitive motions, π₀ is designed to be a generalist policy that can understand visual inputs, interpret natural language instructions, and control a variety of different robots across diverse tasks. This model is featured as an example in the "Robot Learning: A Tutorial" paper.
 
 ### Architecture and Approach
 
 π₀ combines several key innovations:
 
-- **Flow Matching**: Uses a novel method to augment pre-trained VLMs with continuous action outputs via flow matching (a variant of diffusion models)
-- **Cross-Embodiment Training**: Trained on data from 8 distinct robot platforms including UR5e, Bimanual UR5e, Franka, Bimanual Trossen, Bimanual ARX, Mobile Trossen, and Mobile Fibocom
-- **Internet-Scale Pre-training**: Inherits semantic knowledge from a pre-trained 3B parameter Vision-Language Model
-- **High-Frequency Control**: Outputs motor commands at up to 50 Hz for real-time dexterous manipulation
+-   **Flow Matching**: Uses a novel method to augment pre-trained VLMs with continuous action outputs via flow matching (a variant of diffusion models)
+-   **Cross-Embodiment Training**: Trained on data from 8 distinct robot platforms including UR5e, Bimanual UR5e, Franka, Bimanual Trossen, Bimanual ARX, Mobile Trossen, and Mobile Fibocom
+-   **Internet-Scale Pre-training**: Inherits semantic knowledge from a pre-trained 3B parameter Vision-Language Model
+-   **High-Frequency Control**: Outputs motor commands at up to 50 Hz for real-time dexterous manipulation
 
 ## Training
 
@@ -40,7 +54,7 @@ python src/lerobot/scripts/train.py \
 
 ## Citation
 
-If you use this model, please cite the original OpenPI work:
+If you use this model, please cite the original OpenPI work and the tutorial paper:
 
 ```bibtex
 @article{openpi2024,
@@ -49,12 +63,18 @@ If you use this model, please cite the original OpenPI work:
     year={2024},
     url={https://github.com/Physical-Intelligence/openpi}
 }
-```
 
-## Original Repository
-
-[OpenPI GitHub Repository](https://github.com/Physical-Intelligence/openpi)
+@misc{tutorial2025robotlearning,
+      title={Robot Learning: A Tutorial},
+      author={Francisco Cruz and Niels Rogge and Victor Dibia and Sasha Bozhkov and Thomas Wolf},
+      year={2025},
+      eprint={2510.12403},
+      archivePrefix={arXiv},
+      primaryClass={cs.RO},
+      url={https://arxiv.org/abs/2510.12403},
+}
+```
 
 ## License
 
-This model follows the same license as the original OpenPI repository.
+This model follows the same license as the original OpenPI repository, which is Apache 2.0.