スタイン変分モデル予測制御
Stein Variational Model Predictive Control
MPCをベイズ推論として定式化し、スタイン変分勾配降下法で制御パラメータ上の事後分布を直接推定することで、非凸な最適制御問題でも計画を成功させる手法を提案した。
著者: Alexander Lambert, Adam Fishman, Dieter Fox, Byron Boots, Fabio Ramos
分類: cs.RO, cs.AI
原文アブストラクト
Decision making under uncertainty is critical to real-world, autonomous systems. Model Predictive Control (MPC) methods have demonstrated favorable performance in practice, but remain limited when dealing with complex probability distributions. In this paper, we propose a generalization of MPC that represents a multitude of solutions as posterior distributions. By casting MPC as a Bayesian inference problem, we employ variational methods for posterior computation, naturally encoding the complexity and multi-modality of the decision making problem. We present a Stein variational gradient descent method to estimate the posterior directly over control parameters, given a cost function and observed state trajectories. We show that this framework leads to successful planning in challenging, non-convex optimal control problems.
