Hugging Face, the open-source AI powerhouse, has taken a significant step towards democratizing low-cost robotics with the release of a detailed tutorial that guides developers through the process of building and training their own AI-powered robots. The tutorial, published today, builds upon the company’s LeRobot platform launched in May and marks a significant move to bring artificial intelligence into the physical world. This initiative marks a pivotal moment in the field of robotics, traditionally dominated by large corporations and research institutions with substantial resources. By providing a comprehensive guide that covers everything from sourcing parts to deploying AI models, Hugging Face is empowering developers of all skill levels to experiment with cutting-edge robotics technology. Remi Cadene, a principal research scientist at Hugging Face and a key contributor to the project, describes the tutorial as a way to “unlock the power of end-to-end learning—like LLMs for text, but designed for robotics.” In a series of tweets, Cadene highlighted the potential for developers to train neural networks that predict motor movements directly from camera images, mirroring the way large language models (LLMs) process text. “You will learn how to train a neural network to directly predict the next motor rotations straight from camera images,” Cadene explained, underscoring the tutorial’s focus on practical, real-world applications of AI in robotics. Central to the tutorial is the Koch v1.1, an affordable robotic arm designed by Jess Moss. This version improves upon Alexander Koch’s original design, featuring a simplified assembly process and enhanced capabilities. “We first guide you to our bill of materials to order your robot parts (in $, £ or €),” Cadene tweeted, emphasizing the project’s global accessibility.
Full story : Hugging Face’s open source LeRobot tutorial is a game-changer.
