论文标题
SACBP：通过随机顺序动作控制连续时间动态系统的信念空间规划
SACBP: Belief Space Planning for Continuous-Time Dynamical Systems via Stochastic Sequential Action Control
论文作者
论文摘要
我们通过将信念系统视为具有时间驱动的切换，为连续动力学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扰动理论，并将顺序作用控制扩展到随机动力学。我们称之为SACBP的结果算法不需要空间或时间离散化，并在几乎实时综合了控制信号。 SACBP是任何时间算法，可以在某些假设下处理一般参数贝叶斯过滤器。我们在积极的感应场景和基于模型的贝叶斯强化学习问题中证明了方法的有效性。在这些具有挑战性的问题中，我们表明该算法显着优于其他现有解决方案技术，包括近似动态编程和局部轨迹优化。
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.
