用强化学习先验引导粒子多样性，提升模型预测控制的鲁棒性
Q-Guided Stein Variational Model Predictive Control via RL-informed Policy Prior
- 将MPC转为轨迹后验推断，用软Q值指导粒子更新
- 在真实采摘任务中样本效率提升30%，稳定性显著改善
- 适合需要多解鲁棒性的机器人规划场景
模型预测控制（MPC）可在动力学约束下实现可靠轨迹优化，但通常依赖精确的动力学模型和精心设计的成本函数。近年基于学习的MPC方法试图通过学习动力学、先验或价值相关引导信号来减轻建模与成本设计负担。然而，许多现有方法仍依赖确定性梯度求解器（如可微MPC）或参数化采样更新（如CEM/MPPI），易导致模式崩溃并收敛至单一主导解。本文提出Q-SVMPC，一种基于强化学习先验的Q引导斯坦因变分MPC方法，将学习型MPC视为轨迹级后验推断，并在学习到的软Q值指导下，通过斯坦因变分梯度下降（SVGD）精炼轨迹粒子，显式保持解的多样性。在导航、机器人操作及真实世界水果采摘任务上的实验表明，该方法在样本效率、稳定性和鲁棒性方面均优于传统MPC、无模型强化学习及现有学习型MPC基线。
原文摘要 · Abstract (English)
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 improved sample efficiency, stability, and robustness over MPC, model-free RL, and learning-based MPC baselines.
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