[2603.17834] Generative Control as Optimization: Time Unconditional Flow Matching for Adaptive and Robust Robotic Control

Computer Science > Robotics arXiv:2603.17834 (cs) [Submitted on 18 Mar 2026 (v1), last revised 26 Apr 2026 (this version, v2)] Title:Generative Control as Optimization: Time Unconditional Flow Matching for Adaptive and Robust Robotic Control Authors:Zunzhe Zhang, Runhan Huang, Yicheng Liu, Shaoting Zhu, Linzhan Mou, Hang Zhao View a PDF of the paper titled Generative Control as Optimization: Time Unconditional Flow Matching for Adaptive and Robust Robotic Control, by Zunzhe Zhang and 5 other authors View PDF HTML (experimental) Abstract: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 opt...