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

ArXi:2603.17834v2 Announce Type: replace-cross 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