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].

Core Viewpoint - The forum emphasizes the rapid advancements in embodied intelligence and robotics, highlighting the need for a unique computational brain that can translate computational power into physical capabilities, addressing the gap between AI's performance in games like Go and its struggles with simple physical tasks [4]. Group 1: Evolution of Embodied Intelligence - Over the past decade, embodied intelligence has evolved significantly, with robotics being a closed-loop system that integrates perception, action, and the physical world, emphasizing the importance of adhering to physical laws [5][6]. - The gap between research prototypes and practical applications is highlighted, with the Technology Readiness Level (TRL) being a key metric for assessing the maturity of robotic applications, where levels 8 to 9 are crucial for industry acceptance [6]. Group 2: Opportunities and Challenges in Robotics - The forum discusses the historical context of machine learning's impact on robotics, noting that advancements in sensors, algorithms, and deep learning have led to significant progress, but achieving high performance in the physical world remains a challenge [9][13]. - The importance of scalable learning systems is emphasized, with a shift from small-scale learning to large-scale applications being crucial for overcoming challenges in robotics [15]. 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 [23][25]. - The advantages of specialized models include efficiency, robustness, and suitability for real-time applications, while general models offer greater flexibility but are more complex and resource-intensive [27][30]. Group 4: Future Directions in Robotics - The emergence of visual-language-action (VLA) models, such as RT-2, represents a significant step forward, allowing robots to execute tasks through internet-based API calls, indicating a trend towards more versatile robotic capabilities [39][40]. - The development of the second-generation VLA model, PI-Zero, showcases advancements in continuous action generation, enabling robots to perform complex tasks with higher efficiency [46][48]. Group 5: Data and Performance in Robotics - The forum highlights the necessity of large-scale data collection for training robotic models, with the RTX dataset being a pivotal resource for developing cross-embodied models that outperform specialized counterparts [42][43]. - The importance of performance metrics is underscored, with a focus on achieving high reliability and robustness in robotic systems to ensure practical deployment in real-world scenarios [58][65].