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This paper introduces a generative predictive control (GPC) framework that leverages conditional flow-matching models to amortize sampling-based model predictive control (SPC) for contact-rich manipulation. By training these flow-matching models on SPC control sequences generated in simulation, the method learns proposal distributions that enable more efficient and informed sampling during online planning compared to methods relying on iterative refinement or gradient-based solvers. The approach is validated through extensive experiments in simulation and on a quadruped robot performing real-world loco-manipulation, demonstrating improved sample efficiency, reduced planning horizon requirements, and robust generalization.
Ditch slow iterative refinement: conditional flow-matching models can directly learn meaningful proposal distributions from noisy sampling-based MPC data, slashing planning time.
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.
NVIDIA