Q-Guided Stein Variational Model Predictive Control via RL-informed Policy Prior
Shizhe CaiZeya YinJayadeep JacobFabio Ramos
Sep 2026
Artificial IntelligenceMachine LearningRobotics
Abstract
Model Predictive Control (MPC) enables reliable trajectory optimization under dynamics constraints, but often depends on accurate dynamics models and carefully hand-designed cost functions. Recent learning-based MPC methods aim to reduce these modeling and cost-design burdens by learning dynamics, priors, or value-related guidance signals. Yet many existing approaches still rely on deterministic gradient-based solvers (e.g., differentiable MPC) or parametric sampling-based updates (e.g., CEM/MPPI), which can lead to mode collapse and convergence to a single dominant solution. We propose Q-SVMPC, a Q-guided Stein variational MPC method with an RL-informed policy prior, which casts learning-based MPC as trajectory-level posterior inference and refines trajectory particles via SVGD under learned soft Q-value guidance to explicitly preserve diverse solutions. Experiments on navigation, robotic manipulation, and a real-world fruit-picking task show competitive learning efficiency, strong final performance, and training stability compared with MPC, model-free RL, and learning-based MPC baselines.
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