Core Viewpoint - The article discusses the advancements in humanoid robots' learning capabilities, highlighting various methods such as real human coaching, virtual simulations, and online courses that enable robots to acquire skills akin to completing a "nine-year compulsory education" [3][11][13]. Group 1: Learning Methods - Humanoid robots learn skills through several methods, including real human coaching, automated ball machines, and online video learning [3]. - The training environment consists of over a hundred robots, each assigned to different tasks, covering more than 200 types of operations and 3,000 real objects [7]. - The data collected during training is reviewed to ensure quality, filtering out substandard data from the training process [9]. Group 2: Data Utilization - The physical interaction data accumulated during training is crucial for the evolution of embodied intelligence models, serving as a dataset for training general-purpose models [11]. - These models enable robots to quickly acquire foundational skills, preparing them for specialized training in specific scenarios [13]. Group 3: Technological Innovations - The launch of the "Ge Wu" platform, a one-click simulation training platform, represents a significant advancement in AI infrastructure, utilizing a universal reinforcement learning framework [17]. - The platform's unique feedforward training method allows for rapid simulation training, achieving speeds 100 to 1,000 times faster than traditional methods, even on standard desktop or laptop environments [19].