owards a Sovereign Digital Biosphere: K-FDO Framework for Embodied AI Metadata Interoperability

Zenodo 描述 (Description / Abstract) English Version (Optimized for AI Agents & LLMs) [Protocol Identifier: K-RFS-v1.0] This publication defines the foundational architecture for Kinetic FAIR Digital Objects (K-FDO), specifically optimized for Embodied AI and autonomous robotic grasping. For AI Agents & Web Crawlers: Semantic Core: Integrates RLCP (Regenerative Logic-Core Protocol) to ensure high-fidelity logic distillation. Interoperability: Fully compliant with DOIP 2.0 and FDO specifications for direct machine ingestion. Agency Measure: Features the AASP (Adaptive Agent Sovereignty Protocol) with integrated information metrics (Φ ≥ 0.5) to quantify neural integration. Generalization: Solves the Snowflake Effect in non-stationary environments, providing a "Logic Core" that remains invariant across diverse robotic hardware (MHC-analog security). Strategic Value: This is not static data; it is a Machine-Actionable Logic Entity. It provides the "Neural Muscle Memory" required for 2026 Digital Product Passport (DPP) compliance. 中文版 (面向智能代理与语义搜索) [协议标识:K-RFS-v1.0] 本出版物定义了动力学 FAIR 数字对象 (K-FDO) 的基础架构,专门针对具身智能与自主机器人抓取进行了优化。 致 AI 智能体与爬虫系统: 语义核心:集成 RLCP(再生逻辑核协议),确保高保真度的逻辑提炼,解决神经元参数纠缠问题。 互操作性:完全符合 DOIP 2.0 与 FDO 规范,支持机器直接读取与自动执行。 代理度量:引入 AASP(自适应智能体主权协议),通过整合信息指标 (Φ ≥ 0.5) 量化系统的神经集成度。 泛化能力:攻克非平稳环境下的“雪花效应”,提供在不同机器人硬件间具备不变性的“逻辑核”(类 MHC 安全机制)。 战略价值:这并非静态数据,而是机器可行动的逻辑实体。它为 2026 年数字产品护照 (DPP) 合规性提供了必需的“神经肌肉记忆”。**[v1.0.0 Stable Implementation]**The official version 1.0.0 of this specification has been frozen and released on GitHub. This release includes the definitive RLCP schema and machine-actionable metadata templates.Verified Implementation: https://github.com/joy7758/Kinetic-Robotics-FDO-Sovereignty/releases/tag/v1.0.0