Unsupervised motion learning from a moving platform

Victor A. Romero-Cano, Juan I. Nieto, Gabriel Agamennoni
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引用次数: 2

Abstract

Learning motion patterns in dynamic environments is a key component of any context-aware robotic system, and probabilistic mixture models provide a sound framework for mining these patterns. This paper presents an approach for learning motion models from trajectories provided by the tracking system of a moving platform. We present a learning approach in which a Linear Dynamical System (LDS) is augmented with a discrete hidden variable that has a number of states equal to the number of behaviours in the environment. As a result, a mixture of linear dynamical systems (MLDSs) capable of explaining several motion behaviours is developed. The model is learned by means of the Expectation Maximization (EM) algorithm.
从移动平台进行无监督运动学习
在动态环境中学习运动模式是任何上下文感知机器人系统的关键组成部分,概率混合模型为挖掘这些模式提供了良好的框架。本文提出了一种从运动平台跟踪系统提供的轨迹中学习运动模型的方法。我们提出了一种学习方法,其中线性动力系统(LDS)被一个离散的隐变量增强,该隐变量具有与环境中行为数量相等的状态数量。因此,能够解释几种运动行为的线性动力系统(mlds)的混合物被开发出来。该模型采用期望最大化(EM)算法进行学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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