基于概率图模型的潜在Kullback-Leibler控制用于连续状态系统
Latent Kullback Leibler Control for Continuous-State Systems using Probabilistic Graphical Models
AI总结：
本文提出将KL控制问题嵌入概率图模型中，通过连续状态和离散表示进行控制，展示两种方法在机器人运动控制中的应用。
摘要：
Kullback Leibler (KL) 控制问题允许通过求解主特征向量问题高效计算最优控制。然而，直接将此类框架应用于连续状态-动作系统受限。本文提出将KL控制问题嵌入概率图模型中，其中观测变量对应系统的连续（可能高维）状态，潜在变量对应状态的离散（低维）表示，适用于KL控制计算。我们展示了两种方法：第一种使用标准隐马尔可夫模型（HMMs）计算精确最优控制，但仅适用于低维系统；第二种使用因子HMMs，可扩展到高维问题，但控制计算为近似。我们通过多个机器人运动控制任务展示了这两种方法。
英文摘要：
Kullback Leibler (KL) control problems allow for efficient computation of optimal control by solving a principal eigenvector problem. However, direct applicability of such framework to continuous state-action systems is limited. In this paper, we propose to embed a KL control problem in a probabilistic graphical model where observed variables correspond to the continuous (possibly high-dimensional) state of the system and latent variables correspond to a discrete (low-dimensional) representation of the state amenable for KL control computation. We present two examples of this approach. The first one uses standard hidden Markov models (HMMs) and computes exact optimal control, but is only applicable to low-dimensional systems. The second one uses factorial HMMs, it is scalable to higher dimensional problems, but control computation is approximate. We illustrate both examples in several robot motor control tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
