Hugging Face has released LeRobot, a new machine learning model specifically designed for real-world robot application development. As a comprehensive platform, LeRobot provides a versatile library that covers functions such as data sharing, visualization, and advanced model training. LeRobot aims to provide PyTorch users with the models, datasets, and tools necessary for real-world robot applications. Its goal is to lower the barrier to entry for robot learning, allowing everyone to contribute to and benefit from the sharing of datasets and pre-trained models. Remi Cadene, an expert who previously worked as a scientist at Tesla and now works at Hugging Face, stated on his personal account: "LeRobot is as important to robot learning as the Transformers library is to natural language processing (NLP)." By providing pre-trained models and seamless integration with physics simulators, LeRobot greatly simplifies the project initiation process. Recently, it was evaluated in the AlohaTransferCube environment and compared to similar models trained using the original ACT library. The test results from 500 fragments demonstrated its success rate and provided valuable insights into its performance. Additionally, LeRobot was evaluated in the PushT environment and compared to models trained using the original Diffusion Policy code. This evaluation also included success metrics from 500 fragments, providing a comprehensive understanding of LeRobot's capabilities in real-world scenarios. LeRobot is dedicated to adapting to various robot hardware, whether it be basic educational robotic arms or complex humanoid robots in research environments. It aims to provide a flexible and adaptable AI system for any type of robot, enhancing the flexibility and scalability of robot applications. LeRobot operates as an open-source project on GitHub, aiming to share power and innovation with a wider community. Hugging Face encourages global developers, researchers, and enthusiasts to participate and contribute to the advancement of AI robotics, benefiting from the free availability of LeRobot. The release of LeRobot has sparked enthusiastic responses in the AI and robotics community. Community members on X have posted cheers: "Let the prosperity of robot learning begin!" While others excitedly expressed: "This is an open-source paradise for robotics enthusiasts!" LeRobot provides datasets that cover various scenarios and tasks in robot learning. These datasets encompass simulated environments for object insertion, transfer, movement challenges, and various object manipulation tasks. For example, there are datasets focused on human-guided actions and scripted transfers, such as aloha_sim_insertion_human_image and aloha_sim_transfer_cube_scripted_image. There are also datasets involving static objects, such as aloha_static_battery and aloha_static_candy. Additionally, there are datasets related to arm motion and manipulation, such as xarm_push_medium_replay_image and xarm_lift_medium_image. These datasets are crucial resources for training and testing AI models in real-world robot applications. The potential of LeRobot in simplifying robot development and its commitment to lowering the entry barrier for contributors make it a promising resource. Although there are still areas that require attention and improvement, such as documentation, hardware compatibility, and performance, its future remains highly anticipated.