《人工智能 具身智能体应用框架及接口规范》标准正式立项

Core Viewpoint - The establishment of the "Artificial Intelligence Embodied Intelligent Agent Application Framework and Interface Specification" standard aims to address the challenges faced by the embodied intelligence industry, including high adaptation costs, poor capability reuse, and high development thresholds due to the lack of unified standards [1][2]. Group 1: Industry Challenges - The embodied intelligence sector is experiencing rapid development but is hindered by issues such as platform heterogeneity, interface fragmentation, and closed functional modules [1]. - The absence of a unified standard leads to three prominent problems: high adaptation costs, poor capability reuse, and high development thresholds [1]. Group 2: Standard Development - The standard is jointly drafted by Beijing Human-Robot Innovation Center, China Electronic Technology Standardization Institute, and other industry stakeholders, aiming to provide a framework and interfaces applicable to humanoid robots, intelligent service robots, and industrial collaborative intelligent agents [2]. - The standard systematically constructs a layered application framework and standardized interface system for embodied intelligent agents, particularly for applications requiring cross-entity access and capability reuse [2][3]. Group 3: Technical Specifications - The standard innovatively defines a modular framework structure consisting of "basic model layer - basic module layer - open function layer," clarifying the functional boundaries and relationships of each layer [3]. - It specifies four core interfaces: control interface, feedback interface, management interface, and application development interface, promoting interoperability and capability reuse among different vendors' AI capabilities and robotic hardware platforms [3][5]. Group 4: Impact on the Industry - The formulation of this standard is a significant achievement in collaborative innovation within the embodied intelligence industry, enhancing system universality and scalability while lowering the integration costs [5]. - It aims to provide stable interfaces for upper-level task development and behavior orchestration, significantly improving development efficiency and supporting the national artificial intelligence standard system [5].