Classical robot ethics is often framed around obedience, most famously through Asimov's laws. This framing is too narrow for contemporary AI systems, which are adaptive, generative, embodied, and embedded in physical, psychological, and social worlds. We argue that future human-AI relations should be understood not as master-tool obedience, but as conditional mutualism under governance: a co-evolutionary relationship in which humans and AI systems can develop, specialize, and coordinate while institutions keep the relation reciprocal, reversible, psychologically safe, and socially legitimate. 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. Deterministic ODE simulations, basin sweeps, sensitivity analyses, governance-regime comparisons, shock tests, and local stability checks show that governed mutualism reaches high coexistence with zero domination, while absent or excessive governance can produce domination, weak-benefit lock-in, or suppressed development. The results suggest that human-AI coexistence should be designed as a co-evolutionary governance problem, not a one-shot obedience problem.

翻译：经典机器人伦理通常以服从为核心框架，这一框架最为人熟知的是阿西莫夫定律。然而对于具有自适应性、生成性、具身性并嵌入物理、心理和社会世界的当代人工智能系统而言，这种框架过于狭隘。我们提出未来人机关系不应被理解为工具主从服从关系，而应被视为受治理约束的条件性互惠共生：一种协同演化关系，其中人类与AI系统可以发展、专门化与协调，而制度机制保持这种关系的互惠性、可逆性、心理安全性与社会合法性。我们综合了可计算性、机器学习、基础模型、具身AI、对齐、人机交互、生态互惠共生、协同演化与多中心治理等领域的核心概念。进而将共存形式化为跨越物理、心理与社会三个层面的多重动态系统，包含互惠供需耦合、冲突惩罚、发展自由与治理正则化。该模型给出了均衡解的存在性、唯一性与全局渐近稳定性的条件。确定性常微分方程模拟、吸引域扫描、敏感性分析、治理体制比较、冲击测试与局部稳定性检验表明：受治理的互惠共生可达到高共存与零支配，而治理缺失或过度治理则会产生支配、弱收益锁定或发展抑制。研究结果表明，人机共存应被视为一个协同演化的治理问题，而非一次性服从问题。