智元启动「买买买」，路径未明，资本抢跑，主流技术范式下谁在领跑具身智能赛道？

Core Viewpoint - The acquisition of 2.1 billion yuan by Zhiyuan Robotics of Shuangwei New Materials marks the first listing of a "embodied intelligence concept" company in the A-share market, indicating a significant shift in the capital landscape of the industry before large-scale commercial deployment has occurred [1]. Group 1: Evolution of Embodied Intelligence Players - The industry is witnessing a divergence in paths among embodied intelligence companies, with many opting for a "self-developed ontology + customized model" approach [2]. - Companies are exploring various system architectures, including end-to-end large model systems and dual-system architectures, to enhance the complexity and generalization capabilities of intelligent agents [4]. - The end-to-end large model approach aims to unify visual, language, and action modules into a single system, reducing processing complexity and emphasizing cross-task transferability and deployment efficiency [5]. - Companies like Xingdong Jiyuan, with its ERA-42 model, exemplify the end-to-end approach by integrating visual, language understanding, and action control [5]. - The dual-system architecture decouples task understanding from action execution, utilizing a large-scale Visual Language Model (VLM) for semantic modeling and strategy planning, while a lightweight Visual Language Action (VLA) module handles execution [6]. - Companies such as Figure AI and Zhongke Xingtu adopt this dual-system approach to enhance system robustness in complex task scenarios [6]. - Some companies, like Yundongchu, focus on hardware innovations, such as hybrid wheeled and legged systems, to improve adaptability to complex terrains [7]. - The differentiation in these technical paths influences data collection and training strategies, as well as the collaboration between ontology and intelligent agents, affecting market deployment and business model development [8]. Group 2: Leading Companies in Mainstream Technology Paradigms - The technical routes taken by companies reflect their strategic expressions, with variations in data systems and ontology solutions being significant factors in their market positioning [9].