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.

我们通过将信念系统视为具有时间驱动切换的混合动力系统，为连续动态提出了一种新颖的信念空间规划技术。我们的方法基于微分方程的微扰理论，并将顺序动作控制扩展到随机动力学。由此产生的算法，我们称之为 SACBP，不需要空间或时间的离散化，并且可以近乎实时地合成控制信号。SACBP 是一种随时算法，可以在某些假设下处理一般的参数贝叶斯滤波器。我们证明了我们的方法在主动感知场景和基于模型的贝叶斯强化学习问题中的有效性。在这些具有挑战性的问题中，