SACBP：確率的逐次行動制御による連続時間力学系の信念空間計画
SACBP: Belief Space Planning for Continuous-Time Dynamical Systems via Stochastic Sequential Action Control
連続時間の確率的力学系に対し、微分方程式の摂動論と逐次行動制御を拡張して、空間や時間の離散化なしに準リアルタイムで制御信号を生成する信念空間計画アルゴリズムSACBPを提案した論文。
著者: Haruki Nishimura, Mac Schwager
分類: math.OC, cs.RO, cs.SY, eess.SY
原文アブストラクト
We propose a novel belief space planning technique for continuous dynamics by viewing the belief system as a hybrid dynamical system with time-driven switching. Our approach is based on the perturbation theory of differential equations and extends Sequential Action Control to stochastic dynamics. The resulting algorithm, which we name SACBP, does not require discretization of spaces or time and synthesizes control signals in near real-time. SACBP is an anytime algorithm that can handle general parametric Bayesian filters under certain assumptions. We demonstrate the effectiveness of our approach in an active sensing scenario and a model-based Bayesian reinforcement learning problem. In these challenging problems, we show that the algorithm significantly outperforms other existing solution techniques including approximate dynamic programming and local trajectory optimization.
