Classical robot ethics is often framed around obedience, including Asimov's laws. This framing is insufficient for contemporary AI systems, which are increasingly adaptive, generative, embodied, and embedded in physical, psychological, and social environments. This paper proposes conditional mutualism under governance as a framework for human-AI coexistence: a co-evolutionary relationship in which humans and AI systems develop, specialize, and coordinate under institutional conditions that preserve reciprocity, reversibility, psychological safety, and social legitimacy. We synthesize concepts from computability, machine learning, foundation models, embodied AI, alignment, human-robot interaction, ecological mutualism, coevolution, and polycentric governance. We then formalize coexistence as a multiplex dynamical system across physical, psychological, and social layers, with reciprocal supply-demand coupling, conflict penalties, developmental freedom, and governance regularization. The model gives conditions for existence, uniqueness, and global asymptotic stability of equilibria. We complement the analytical results with deterministic ODE simulations, basin sweeps, sensitivity analyses, governance-regime comparisons, shock tests, and local stability checks. The simulations indicate that governed mutualism reaches a high coexistence index with negligible domination, whereas insufficient or excessive governance can produce domination, weak-benefit lock-in, or suppressed developmental freedom. The results suggest that human-AI coexistence should be designed as a co-evolutionary governance problem rather than as a static obedience problem.

翻译：经典机器人伦理常以服从性为框架，如阿西莫夫定律。这一框架已不足以描述当代人工智能系统——它们日益具备自适应性、生成性、具身性，并深度嵌入物理、心理与社会环境。本文提出"治理条件下的条件共生"作为人类-人工智能共存的框架：一种人类与AI系统在维系互惠性、可逆性、心理安全与社会合法性的制度条件下，进行发展、专化与协调的协同演化关系。我们综合了可计算性理论、机器学习、基础模型、具身智能、对齐、人机交互、生态共生、协同演化与多中心治理等概念，进而将共存形式化为一个横跨物理层、心理层与社会层的多重动力学系统，系统包含供需双向耦合、冲突惩罚、发展自由度与治理正则化。该模型给出了均衡存在性、唯一性与全局渐近稳定性的条件。我们通过确定性常微分方程模拟、流域扫描、灵敏度分析、治理体制比较、冲击检验与局部稳定性检验对分析结果进行补充。模拟结果表明，受治理的共生能实现高共存指数且支配性极低，而治理不足或过度则会导致支配性、弱收益锁定或发展自由度受抑。研究结果提示，人类-人工智能共存应被设计为协同演化治理问题，而非静态服从问题。