Diffusion models and flow matching have become a cornerstone of robotic imitation learning, yet they suffer from a structural inefficiency where inference is often bound to a fixed integration schedule that is agnostic to state complexity. This paradigm forces the policy to expend the same computational budget on trivial motions as it does on complex tasks. We introduce Generative Control as Optimization (GeCO), a time-unconditional framework that transforms action synthesis from trajectory integration into iterative optimization. GeCO learns a stationary velocity field in the action-sequence space where expert behaviors form stable attractors. Consequently, test-time inference becomes an adaptive process that allocates computation based on convergence--exiting early for simple states while refining longer for difficult ones. Furthermore, this stationary geometry yields an intrinsic, training-free safety signal, as the field norm at the optimized action serves as a robust out-of-distribution (OOD) detector, remaining low for in-distribution states while significantly increasing for anomalies. We validate GeCO on standard simulation benchmarks and demonstrate seamless scaling to pi0-series Vision-Language-Action (VLA) models. As a plug-and-play replacement for standard flow-matching heads, GeCO improves success rates and efficiency with an optimization-native mechanism for safe deployment. Video and code can be found at https://hrh6666.github.io/GeCO/

翻译：扩散模型与流匹配已成为机器人模仿学习的基石，但其存在结构性低效问题：推理过程通常绑定至固定积分调度，且对不同状态复杂度缺乏感知。这种范式迫使策略在简单运动和复杂任务上消耗相同的计算预算。我们提出生成控制即优化（GeCO），一种将动作合成从轨迹积分转化为迭代优化的时间无约束框架。GeCO在动作序列空间中学习稳态速度场，使专家行为形成稳定吸引子。由此，测试时推理成为自适应过程：针对简单状态提前终止计算，针对困难状态延长优化周期。此外，该稳态几何结构天然生成无需训练的安全信号——优化动作处的场范数可作为鲁棒性分布外（OOD）检测器，对分布内状态保持低响应，而对异常状态显著增强。我们在标准仿真基准上验证GeCO，并展示其无缝扩展至pi0系列视觉-语言-动作（VLA）模型的能力。作为标准流匹配头的即插即用替代方案，GeCO通过优化原生的安全部署机制提升了成功率和效率。视频与代码详见https://hrh6666.github.io/GeCO/