纯血VLA综述来啦！从VLM到扩散，再到强化学习方案

Core Insights - The article discusses the evolution and potential of Vision Language Action (VLA) models in robotics, emphasizing their integration of perception, language understanding, and action generation to enhance robotic capabilities [11][17]. Group 1: Introduction and Background - Robotics has traditionally relied on pre-programmed instructions and control strategies, limiting their adaptability in dynamic environments [2][11]. - The emergence of VLA models marks a significant advancement in embodied intelligence, combining visual perception, language understanding, and executable actions into a unified framework [11][12]. Group 2: VLA Methodologies - VLA methods are categorized into four paradigms: autoregressive, diffusion, reinforcement learning, and hybrid/specialized methods, each with unique strategies and mechanisms [8][10]. - The article highlights the importance of high-quality datasets and realistic simulation platforms for the development and evaluation of VLA models [16][18]. Group 3: Challenges and Future Directions - Key challenges identified include data limitations, reasoning speed, and safety concerns, which need to be addressed to advance VLA models and general robotics [10][17]. - Future research directions focus on enhancing the robustness and generalization of VLA models in real-world applications, emphasizing the need for efficient training paradigms and safety assessments [44][47].