3个月！搞透具身大脑+小脑算法

Core Viewpoint - The exploration of Artificial General Intelligence (AGI) is increasingly focusing on embodied intelligence, which emphasizes the interaction and adaptation of intelligent agents within physical environments, enabling them to perceive, understand tasks, execute actions, and learn from feedback [1]. Industry Analysis - In the past two years, numerous star teams in the field of embodied intelligence have emerged, establishing valuable companies such as Xinghaitu, Galaxy General, and Zhujidongli, driving advancements in embodied intelligence technologies [3]. - Major domestic companies like Huawei are launching initiatives such as the "Global Embodied Intelligence Industry Innovation Center" in collaboration with firms like Leju Robotics and Dazhu Robotics to develop key technologies for embodied intelligence [5]. - JD.com has been investing in companies like Zhiyuan Robotics and Qianxun Intelligent since May 2025 to enhance efficiency and service capabilities in logistics and home service scenarios [5]. - Internationally, companies like Tesla and Figure AI are advancing applications in industrial and logistics robotics, while U.S. investment firms are supporting companies like Wayve and Apptronik in autonomous driving and warehouse robotics [5]. Technological Evolution - The development of embodied intelligence has progressed through several stages, from low-level perception to high-level task understanding and generalization, aiming to enhance robots' capabilities in real-world environments [6]. - The first stage focused on grasp pose detection, enabling robots to predict suitable end-effector poses for static object manipulation, but lacked context modeling for complex tasks [6]. - The second stage introduced behavior cloning, allowing robots to learn from expert demonstrations, yet faced challenges in generalization and performance in multi-target scenarios [6]. - The third stage, emerging in 2023, utilized Diffusion Policy methods to improve stability and generalization by modeling action trajectories [7]. - The fourth stage, starting in 2025, explores the integration of VLA models with reinforcement learning and tactile sensing to overcome limitations in feedback and future prediction capabilities [8]. Product and Market Development - The evolution from grasp pose detection to behavior cloning and VLA models signifies a shift towards intelligent agents capable of handling general tasks in open environments, leading to the emergence of various products like humanoid robots and robotic arms across industries such as healthcare and logistics [9]. - The demand for engineering and system capabilities is increasing as embodied intelligence transitions from research to deployment, necessitating higher engineering standards [12].