Generative Control as Optimization: Time Unconditional Flow Matching for Adaptive and Robust Robotic Control
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...
| Authors | Zunzhe Zhang, Runhan Huang, Yicheng Liu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 18 March 2026 |
| Last updated at source | 26 April 2026 |
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