万字长文聊具身智能“成长史”：具身智能跨越了哪些山海，又将奔向哪里

Core Viewpoint - The article discusses the rapid advancements in embodied intelligence and robotics, emphasizing the need for robots to integrate AI with physical capabilities to perform tasks that are currently challenging for them, such as simple actions that children can do [8][9]. Group 1: Evolution of Embodied Intelligence - Over the past decade, embodied intelligence has evolved significantly, with a focus on integrating AI into robots' control systems to enhance their performance in the physical world [9]. - The gap between research prototypes and practical applications is highlighted, with a need for robots to reach a Technology Readiness Level (TRL) of 8 to 9 for industrial acceptance [10]. - Machine learning advancements, including better sensors and algorithms, have led to substantial improvements in robotics, but achieving high success rates in real-world applications remains a challenge [12][14]. Group 2: Opportunities and Challenges in Robotics - The current landscape presents both opportunities and challenges for robotics, with a focus on structured environments for initial applications before tackling more complex, unstructured settings [14][17]. - The importance of scalable learning systems in robotics is emphasized, as researchers aim to leverage data from multiple robots to enhance performance across various tasks [20]. Group 3: Specialized vs. General Intelligence - The discussion contrasts Artificial Specialized Intelligence (ASI) with Artificial General Intelligence (AGI), suggesting that while ASI focuses on high performance in specific tasks, AGI aims for broader capabilities [27][29]. - The advantages of specialized models include efficiency, robustness, and the ability to run on-premise, while general models offer greater flexibility but are more complex and costly to operate [31][35]. Group 4: Future Directions in Robotics - The emergence of visual-language-action (VLA) models, such as RT-2, represents a significant step forward in robotics, allowing for more complex task execution through remote API calls [44][46]. - The development of the RTX dataset, which includes diverse robotic data, has shown that cross-embodied models can outperform specialized models in various tasks, indicating the potential for generalization in robotics [47][48]. - The second-generation VLA models, like PI-Zero, are designed to handle continuous actions and complex tasks, showcasing advancements in robot dexterity and adaptability [49][50]. Group 5: Data and Performance in Robotics - The importance of data in achieving high performance in robotics is underscored, with a call for large-scale data collection to support the development of robust robotic systems [62][70]. - The article concludes with a discussion on the need for a balance between performance and generalization in robotics, suggesting that achieving high performance is crucial for real-world deployment [66][68].