技术干货：VLA(视觉-语言-动作)模型详细解读（含主流玩家梳理）

Core Viewpoint - The article focuses on the emerging Vision-Language-Action (VLA) model, which integrates visual perception, language understanding, and action generation, marking a significant advancement in robotics and embodied intelligence [1][2]. Summary by Sections VLA Model Overview - The VLA model combines visual language models (VLM) with end-to-end models, representing a new generation of multimodal machine learning models. Its core components include a visual encoder, a text encoder, and an action decoder [2]. - The VLA model enhances the capabilities of traditional VLMs by enabling human-like reasoning and global understanding, thus improving its interpretability and usability [2][3]. Advantages of VLA Model - The VLA model allows robots to weave language intent, visual perception, and physical actions into a continuous decision-making flow, significantly shortening the gap between instruction understanding and task execution. This enhances the robot's ability to understand and adapt to complex environments [3]. Challenges of VLA Model - The VLA model faces several challenges, including: - Architectural inheritance, where the overall structure is not redesigned but only output modules are added or replaced [4]. - Action tokenization, which involves representing robot actions in a language format [4]. - End-to-end learning, integrating perception, reasoning, and control [4]. - Generalization issues, as pre-trained VLMs may struggle with cross-task transfer [4]. Solutions and Innovations - To address these challenges, companies are proposing a dual-system architecture that separates the VLA model into VLM and action execution models, potentially leading to more effective implementations [5][6]. Data and Training Limitations - The VLA model's training requires large-scale, high-quality multimodal datasets, which are difficult and costly to obtain. The lack of commercial embodied hardware limits data collection, making it challenging to build a robust data cycle [7]. - Additionally, the VLA model struggles with long-term planning and state tracking, as the connection between the "brain" (VLM) and "small brain" (action model) relies heavily on direct language-to-action mapping, leading to issues in handling multi-step tasks [7].