TaiChi-OS:基于太极统一场论范式的全场景智能机器 人操作系统研究 TaiChi-OS: Research on a Full-Scenario Intelligent Robot Operating System Based on the Taiji Unified Field Theory Paradigm

Most mainstream intelligent robot software stacks are built on ROS2 combined with model predictive control (MPC), where mode switching and fault protection rely heavily on manually configured hard thresholds. Under external disturbance, component wear and sensor zero-drift, performance tends to decay, and fault handling often degenerates into an abrupt 'all-or-nothing' brake action, lacking a smooth degradation and self-healing mechanism that evolves from quantitative accumulation to qualitative phase transition. Meanwhile, the information flow and energy flow remain separated, system residuals keep accumulating during long-term operation, and there is no hardware-level safety veto after large language models are integrated as high-level decision makers. Drawing on the abstract dynamical paradigm of the Taiji Unified Field Theory (TUFT), this paper performs a paradigm transfer and proposes the TaiChi-OS full-scenario intelligent robot operating system, positioned in the robot domain similarly to iOS/Android/HarmonyOS in the smartphone domain. It reuses a mature RTOS as the hardware scheduling base, builds a Taiji Modulation Control Framework (TMCF) kernel that establishes information-energy dual yin-yang fields, and introduces the order parameter K (the 'living counterweight') evolution dynamics to realize three-state phase-transition judgement, five-level chained risk protection, information-entropy control and a sleep-like self-repair mechanism. The system provides plug-in and deep-coupling modes compatible with existing MPC/WBC controllers, and achieves hardware independence through a HAL abstraction layer, supporting quadrupeds, humanoids, manipulators and multi-robot groups, with a quadruped as the primary simulation prototype. Multi-group simulations reproduce the endogenous bifurcation and the closed hysteresis loop, and confirm the necessity of the observer under sensor noise and mechanical drift. three-level progressive physical experiment scheme with five falsifiable predictions i designed, with confounder identification described. Mechanism simulation has bee closed-loop verified; physical parameter calibration, prototype compariso experiments and open-source ecosystem work are the next steps 当前智能机器人主流软件栈多以 ROS2 结合模型预测控制(MPC)为核心,系 统模式切换、故障安全防护高度依赖人工设定硬阈值。在外部扰动、部件磨损、 传感器零点漂移工况下,容易出现性能衰减,故障处置常表现为“非 0 即 1” 的突变抱闸,缺少从量变累积到质变相变的平滑降级自愈机制,同时存在信息 流与能量流相互割裂、长时间运行系统残差不断累积、接入大语言模型后缺少 底层硬件级安全拦截等现实痛点。本文借鉴太极统一场论(TUFT)抽象动力学 范式,完成范式迁移,提出 TaiChi-OS 全场景智能机器人操作系统,对标智能手 机领域 iOS、Android、鸿蒙的系统定位。底层复用成熟实时操作系统作为硬件 调度底座;内核构建 TMCF(太极调制框架),建立信息-能量双阴阳场,引入 活秤砣序参量 K 演化动力学,实现三态相变判断、五级链式风险分级防护、信 息熵管控与类睡眠自修复机制;系统提供轻量外挂、深度耦合两种模式兼容现 有 MPC/WBC 控制算法,通过硬件抽象 HAL 层实现硬件无关,可适配四足、人形、 机械臂、多机器人群组多类智能机器人硬件,四足机器人为本系统首要仿真验 证原型。本文完成多组数字仿真,复现内生分岔与闭合迟滞环现象,验证观测 器应对传感器噪声、机械漂移的必要性;设计三级递进实物实验方案与五条可 证伪核心预言,明确各类混杂干扰甄别手段。现阶段已完成机理仿真闭环,实 物参数标定、样机对照实验与开源生态建设为后续重点工作。