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

TL;DR

GeCO introduces a novel, adaptive optimization-based framework for robotic control that improves efficiency and safety by dynamically allocating computation and detecting anomalies without additional training.

Contribution

The paper presents GeCO, a time-unconditional, optimization-based approach that replaces fixed inference schedules with adaptive optimization, enhancing robustness and safety in robotic control.

Findings

GeCO achieves higher success rates on simulation benchmarks.

It enables adaptive inference that saves computation on simple states.

The method provides a training-free safety signal for anomaly detection.

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 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/