VLA-OS：NUS邵林团队探究机器人VLA做任务推理的秘密

Core Viewpoint - The article discusses a groundbreaking research study by a team from the National University of Singapore, focusing on the VLA-OS framework, which systematically analyzes and dissects task planning and reasoning in Vision-Language-Action (VLA) models, aiming to provide insights for the next generation of general-purpose robotic VLA models [2][4]. Group 1: VLA-OS Overview - VLA-OS is a structured framework that includes a clear codebase, multimodal task planning datasets, and standardized training processes for VLA models [4][5]. - The framework aims to unify various VLA paradigms and facilitate controlled experiments to identify effective task planning representations and paradigms [19][20]. Group 2: VLA Model Paradigms - The article outlines two main approaches for integrating task reasoning into VLA models: Integrated-VLA, which combines task planning and policy learning, and Hierarchical-VLA, which separates these functions into different models [10][12]. - Current VLA models exhibit significant variability in architecture, training methods, and task planning representations, complicating performance assessments [13][15]. Group 3: Experimental Findings - The research identifies 14 key findings from over 100 experiments, highlighting the advantages of visual planning representations over language-based ones and the superior performance of Hierarchical-VLA compared to Integrated-VLA [34][35]. - Findings indicate that Integrated-VLA benefits from implicit task planning, while Hierarchical-VLA demonstrates better generalization capabilities [51][52]. Group 4: Recommendations for Future Research - The article suggests prioritizing visual representation planning and goal image planning, with language planning as a supplementary approach [68]. - It emphasizes the importance of task planning pre-training and the need for efficient training mechanisms to avoid gradient conflicts between planning and action outputs [73].