Core Viewpoint - The article discusses the advancements in robotic arms, particularly focusing on the development of Large Behavior Models (LBM) that enable robots to perform complex tasks autonomously, moving beyond simple operations to more intricate manipulations [3][8][14]. Group 1: Development of Robotic Arms - Traditional robotic arms are primarily associated with simple tasks like grabbing or serving ice cream, but the complexity of tasks such as setting a table or assembling a bicycle presents significant challenges [1][2]. - Recent advancements in Visual-Language-Action (VLA) models have allowed robots to integrate multimodal information and execute complex tasks, although the research has not yet reached a milestone level [3][4]. Group 2: Large Behavior Models (LBM) - The LBM is a new approach that builds on VLA concepts, utilizing diffusion model strategies to create a large-scale behavior model capable of executing complex operations [8][14]. - The research conducted by the Toyota Research Institute (TRI) and other institutions has shown that LBM can significantly improve performance in multitask robotic operations, even with limited training data [10][15]. Group 3: Experimental Findings - The study involved training LBMs on approximately 1,700 hours of robot data and conducting over 1,800 real-world evaluations, demonstrating that even with a few hundred hours of diverse data, significant performance improvements can be achieved [15][16]. - The findings indicate that LBM can learn new tasks with 3-5 times less data compared to traditional single-task strategies, showcasing its robustness in various environments [17][20]. Group 4: Evaluation Metrics - The performance of the LBM was assessed using success rates and task completion metrics, with a focus on distinguishing between nearly completed tasks and those that were not executed at all [25][26]. - The evaluation process included both real-world and simulated environments, ensuring a comprehensive assessment of the model's capabilities [29][30]. Group 5: Implications for the Future - The positive results from the LBM research suggest a promising future for general-purpose large-scale models in robotics, hinting at the potential for achieving embodied intelligence akin to a "GPT moment" in the field [16][17]. - The study emphasizes the importance of pre-training and the potential for a virtuous cycle of data acquisition and performance enhancement, indicating that significant advancements can be made even without vast amounts of data [16][49].