This presentation will introduce the audience to a new, emerging body of research on sequential Monte Carlo techniques in robotics. In recent years, particle filters have solved several hard perceptual robotic problems. Early successes were limited to low-dimensional problems, such as the problem of robot localization in environments with known maps. More recently, researchers have begun exploiting structural properties of robotic domains that have led to successful particle filter applications in spaces with as many as 100,000 dimensions. The presentation will discuss specific tricks necessary to make these techniques work in real - world domains,and also discuss open challenges for researchers IN the UAI community.
翻译:本次演讲将介绍一组新的、新兴的关于连续的蒙特卡洛机器人技术的研究对象。近些年来,粒子过滤器已经解决了若干硬感知机器人问题。早期的成功仅限于一些低维问题,如已知地图环境中的机器人定位问题。最近,研究人员开始利用机器人域的结构特性,从而在多达100,000个维度的空间成功地应用粒子过滤器。这次演讲将讨论使这些技术在现实世界领域发挥作用所需的具体技巧,并讨论UAI社区研究人员面临的公开挑战。