每当有人咨询具身入门的路线时，我一定会推荐这套完整的教程

Core Insights - The article discusses the evolution and components of embodied intelligence, focusing on the roles of the "brain" and "cerebellum" in robotics, where the brain handles perception and planning, while the cerebellum is responsible for execution [3][12]. Technical Development - Embodied intelligence has progressed through several stages: from grasp pose detection to behavior cloning, and now to diffusion policy and VLA models, indicating a shift from low-level perception to high-level understanding and generalization [7][12]. - The first stage focused on grasp pose detection using point clouds or images for static object manipulation, but lacked context modeling for complex tasks [7]. - The second stage introduced behavior cloning, allowing robots to learn from expert demonstrations, but faced challenges in generalization and performance in multi-target scenarios [7]. - The third stage, marked by the introduction of diffusion policy methods, enhances stability and generalization by modeling entire action trajectories [8]. - The fourth stage, emerging in 2025, explores the integration of VLA models with reinforcement learning and world models, aiming to overcome limitations in feedback and future prediction capabilities [10]. Subfields and Applications - Various subfields within embodied intelligence include simulation, VLA, diffusion policy, and world models, with VLA and world models currently gaining traction in autonomous driving and embodied applications [5][6]. - The integration of tactile sensing and reinforcement learning with VLA models is expected to improve robots' capabilities in complex environments [10]. Industry Impact - The advancements in embodied intelligence are leading to the development of various products, including humanoid robots, robotic arms, and quadrupedal robots, serving industries such as manufacturing, home automation, food service, and healthcare [12]. - The demand for engineering and system capabilities in the industry is increasing as embodied intelligence transitions from research to deployment [17]. Educational Initiatives - The article promotes a comprehensive curriculum designed to teach the full spectrum of embodied intelligence algorithms, catering to both beginners and advanced learners [14][18]. - The course aims to equip participants with practical skills in simulation, model training, and the application of various embodied intelligence techniques [17][25].