Generative Control as Optimization: Time Unconditional Flow Matching for Adaptive and Robust Robotic Control

Diffusion and flow-based generators are increasingly used in robot policies to produce action chunks from visual observations, proprioception, and language instructions. However, closed-loop robot control requires solving action-generation problems of varying difficulty, whereas standard diffusion and flow-matching inference typically follows a fixed integration schedule. We introduce Generative Control as Optimization (GeCO), a time-unconditional framework that turns action synthesis from fixed-time integration into iterative optimization. GeCO learns a stationary velocity field over action sequences, and refines actions until the field norm becomes small. This enables adaptive computation at each planning call: simple states can terminate early, while difficult states can use additional refinement. The same field norm also provides a lightweight signal for non-convergent action generation under distribution shift. GeCO can be instantiated in both diffusion-transformer policies and flow-matching Vision-Language-Action (VLA) systems without changing the surrounding policy interface. Across simulation benchmarks and real-world tasks, GeCO matches or improves task performance while enabling convergence-based inference as a plug-and-play replacement for standard time-conditioned action generators.