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.

翻译：我们提出一种新的信仰空间规划技术,通过将信仰系统视为具有时间驱动转换的混合动态系统来持续动态。我们的方法基于差异方程式的扰动理论,并将序列动作控制扩展至随机动态。我们称之为SACBP的算法并不要求空间或时间的分离和近实时合成控制信号。SACBP是一种随时可以在某些假设下处理一般对称贝叶斯过滤器的算法。我们展示了我们的方法在活跃的感测情景中的有效性和基于模型的贝叶斯强化学习问题。在这些具有挑战性的问题中,我们显示算法大大优于其他现有解决方案技术,包括近似动态的动态编程和本地轨迹优化。