论文标题
隐藏的参数复发状态空间模型，用于改变动态方案
Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios
论文作者
论文摘要
经常性状态空间模型（RSSM）是时间序列数据和系统标识中学习模式的高度表达模型。但是，这些模型假定动力学是固定且不变的，在现实情况下，这种动态很少是这种情况。许多控制应用程序通常表现出具有相似但不相同动态的任务，这些任务可以建模为潜在变量。我们介绍了隐藏的参数复发状态空间模型（HIP-RSSM），该框架为具有低维的潜在因素集的相关动力学系统的一个家庭参数。我们提出了一种对这种高斯图形模型的学习和执行推理的简单有效方法，该模型避免了诸如变异推理之类的近似值。我们表明，HIP-RSSM在现实世界系统和仿真上的几个具有挑战性的机器人基准上都优于RSSM和竞争性的多任务模型。
Recurrent State-space models (RSSMs) are highly expressive models for learning patterns in time series data and system identification. However, these models assume that the dynamics are fixed and unchanging, which is rarely the case in real-world scenarios. Many control applications often exhibit tasks with similar but not identical dynamics which can be modeled as a latent variable. We introduce the Hidden Parameter Recurrent State Space Models (HiP-RSSMs), a framework that parametrizes a family of related dynamical systems with a low-dimensional set of latent factors. We present a simple and effective way of learning and performing inference over this Gaussian graphical model that avoids approximations like variational inference. We show that HiP-RSSMs outperforms RSSMs and competing multi-task models on several challenging robotic benchmarks both on real-world systems and simulations.
