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 embodied intelligence technology [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 increasing its interpretability and human-like characteristics [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 improving their understanding and adaptability to complex environments [3]. - The model's ability to break the limitations of single-task training enables a more generalized and versatile application in various scenarios [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]. - The need for action tokenization, which involves representing robot actions in a language format [4]. - The requirement for end-to-end learning that integrates perception, reasoning, and control [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, enhancing efficiency and effectiveness [5][6]. Data and Training Limitations - The VLA model's training requires large-scale, high-quality multimodal datasets, which are difficult and costly to collect due to the lack of commercial embodied hardware [7]. - The model struggles with long-term planning and state tracking, leading to difficulties in executing multi-step tasks and maintaining logical coherence in complex scenarios [7].