An Approximate Inference Approach to Temporal Optimization in Optimal Control
An Approximate Inference Approach to Temporal Optimization in Optimal Control
复制标题
最优控制中时间优化的近似推理方法
DOI：
--
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
S. Vijayakumar
中科院分区：
文献类型：
--
作者：
K. Rawlik;Marc Toussaint;S. Vijayakumar
基于迭代局部近似的算法为机器人系统的最优控制提供了一种实用的方法。然而，它们通常需要先验地指定时间参数（例如，运动持续时间或达到中间目标的时间点）。在这里，我们提出了一种方法，能够共同优化的时间参数，除了控制命令配置文件。所提出的方法是基于贝叶斯规范的时间制定的最优控制问题，从规范的时间映射到真实的时间参数化的一个额外的控制变量。近似EM算法推导出，有效地优化了运动持续时间和控制命令，提供了第一次，一个实用的方法来解决通用的通过点的问题，在最优控制框架下的一个系统的方式。所提出的方法，这是适用于植物的非线性动态以及任意状态依赖和二次控制成本，评估现实的模拟冗余机器人工厂。
Algorithms based on iterative local approximations present a practical approach to optimal control in robotic systems. However, they generally require the temporal parameters (for e.g. the movement duration or the time point of reaching an intermediate goal) to be specified a priori. Here, we present a methodology that is capable of jointly optimizing the temporal parameters in addition to the control command profiles. The presented approach is based on a Bayesian canonical time formulation of the optimal control problem, with the temporal mapping from canonical to real time parametrised by an additional control variable. An approximate EM algorithm is derived that efficiently optimizes both the movement duration and control commands offering, for the first time, a practical approach to tackling generic via point problems in a systematic way under the optimal control framework. The proposed approach, which is applicable to plants with non-linear dynamics as well as arbitrary state dependent and quadratic control costs, is evaluated on realistic simulations of a redundant robotic plant.