面试的时候，问到了具身的大小脑算法是什么......

Core Insights - The article discusses the evolution and current state 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][10]. Technical Evolution - The development of embodied intelligence has progressed through several stages, starting 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][10]. - 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, emerging in 2023, introduced diffusion policy methods that enhance stability and generalization by modeling action sequences [8]. - The fourth stage, anticipated in 2024, emphasizes the integration of VLA models with reinforcement learning and world models, enhancing robots' predictive capabilities and multi-modal perception [9][10]. Current Trends and Applications - The integration of VLA with reinforcement learning improves robots' trial-and-error capabilities and self-improvement in long-term tasks, while the combination with world models allows for future prediction and better planning [10]. - The industry is witnessing a surge in products related to humanoid robots, robotic arms, and quadrupedal robots, serving various sectors such as industrial, home, dining, and medical rehabilitation [10]. - There is a growing demand for engineering capabilities as embodied intelligence transitions from research to deployment, necessitating skills in simulation and strategy training [14]. Educational Initiatives - The article outlines a structured curriculum aimed at providing comprehensive knowledge of embodied intelligence algorithms, catering to both beginners and advanced learners [11][20]. - The course includes practical applications and supervision to enhance learning outcomes, focusing on various modules such as diffusion policy, VLA, and tactile sensing [11][14].