3个月！完成你的具身大脑+小脑算法学习

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][6]. Industry Analysis - In the past two years, numerous star teams in the field of embodied intelligence have emerged, leading to the establishment of valuable companies such as Xinghaitu, Galaxy General, and Zhujidongli, which are advancing the technology of embodied intelligence [3]. - Major domestic companies like Huawei, JD.com, Tencent, Ant Group, and Xiaomi are actively investing and collaborating to build a comprehensive ecosystem for embodied intelligence, while international firms like Tesla and various U.S. investment institutions are focusing on foundational models and humanoid robot prototypes [5]. Technological Evolution - The development of embodied intelligence has progressed through several stages: - The first stage focused on grasp pose detection, which lacked the ability to model task context and action sequences [6]. - The second stage involved behavior cloning, allowing robots to learn from expert demonstrations but revealing weaknesses in generalization and performance in multi-target scenarios [6]. - The third stage introduced Diffusion Policy methods, enhancing 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, aiming to overcome current limitations and improve robots' planning and decision-making capabilities [8]. Product and Market Development - The evolution of embodied intelligence technologies has led to the emergence of various products, including humanoid robots, robotic arms, and quadrupedal robots, serving industries such as manufacturing, home services, dining, and healthcare [9]. - The demand for engineering and system capabilities is increasing as the industry shifts from research to deployment, necessitating higher engineering skills for effective implementation [12].