机器人何时能迎来自己的“DeepSeek时刻”？

Core Viewpoint - The article discusses the evolution of AI from "cognition" to "action," emphasizing the importance of experience-driven control in achieving practical applications in autonomous driving and robotics [5][6]. Group 1: Experience-Driven Control - The transition from traditional mathematical modeling to experience-driven control is highlighted as essential for real-world applications in complex environments [9][10]. - Experience-driven control allows AI systems to learn from historical data, enabling effective decision-making without precise mathematical models [10][11]. Group 2: Embodied Intelligence - The complexity of embodied intelligence is noted, with a focus on its higher dimensionality compared to autonomous driving, requiring advanced understanding and generalization capabilities [12][14]. - The current state of embodied intelligence is compared to the "DeepSeek moment," indicating that while significant progress has been made, a breakthrough akin to ChatGPT has not yet occurred [15][16]. Group 3: World Models - World models are identified as crucial for enabling robots to understand and interact with the physical world, serving as a foundational element for embodied intelligence [21][25]. - The article outlines three primary uses of world models: facilitating a feedback loop with the robot's brain, generating trajectory data, and integrating physical understanding into robot operations [25][26]. Group 4: Future Directions - The need for world models in the industry is emphasized, particularly for enhancing the generalization capabilities of robots in complex environments [28][31]. - The article suggests that the evolution of world models is still in its early stages, with ongoing developments aimed at improving their application in robotic training and task execution [29][30].