We present a generative predictive control (GPC) framework that amortizes sampling-based Model Predictive Control (SPC) by bootstrapping it with conditional flow-matching models trained on SPC control sequences collected in simulation. Unlike prior work relying on iterative refinement or gradient-based solvers, we show that meaningful proposal distributions can be learned directly from noisy SPC data, enabling more efficient and informed sampling during online planning. We further demonstrate, for the first time, the application of this approach to real-world contact-rich loco-manipulation with a quadruped robot. Extensive experiments in simulation and on hardware show that our method improves sample efficiency, reduces planning horizon requirements, and generalizes robustly across task variations.

翻译：我们提出了一种生成式预测控制（GPC）框架，该框架通过利用在仿真中收集的基于采样的模型预测控制（SPC）控制序列训练的、以条件流匹配模型进行引导，从而摊销基于采样的模型预测控制（SPC）的计算成本。与先前依赖于迭代优化或基于梯度求解器的工作不同，我们证明可以从含噪声的SPC数据中直接学习到有意义的提议分布，从而在在线规划期间实现更高效和更具信息量的采样。我们进一步首次展示了该方法在四足机器人现实世界接触丰富的移动操作中的应用。大量的仿真和硬件实验表明，我们的方法提高了采样效率，降低了对规划视野的要求，并能稳健地泛化到不同的任务变体。