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Passive Distance Learning for Robot Navigation

机译：机器人导航的被动远程学习

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摘要

Autonomous mobile robots need good models of their environment, sensors and actuators to navigate reliably and efficiently. While this information can be supplied by humans, or learned from scratch through active exploration, such approaches are tedious and time-consuming. Our approach is to provide the robot with the topo-logical and geometrical constraints that are easily obtainable by humans, and have the robot learn the rest while in the course of performing its tasks. We present GROW-BW, an unsupervised and passive distance learning algorithm that overcomes the problem that the robot can never be sure about its location if it is not allowed to reduce its uncertainty by asking a teacher or executing localization actions. Advantages of GROW-BW include that the robot can be used immediately to perform navigation tasks and improves its performance over time, focusing its attention to routes that are more relevant for its tasks. We demonstrate that GROW-BW can learn good distance, sensor, and actuator models with only a small amount of experience.
机译：自主移动机器人需要良好的环境模型，传感器和执行器，才能可靠，高效地导航。尽管此信息可以由人类提供，也可以通过积极探索从头开始学习，但是这种方法既繁琐又耗时。我们的方法是为机器人提供人类容易获得的拓扑和几何约束，并让机器人在执行任务的过程中学习其余知识。我们提出了GROW-BW，这是一种无监督且被动的远程学习算法，它克服了如果不允许机器人通过询问老师或执行定位动作来减少不确定性的情况，机器人将永远无法确定其位置的问题。 GROW-BW的优点包括该机器人可立即用于执行导航任务并随着时间的推移提高其性能，将其注意力集中在与其任务更相关的路线上。我们证明，GROW-BW只需很少的经验就可以学习良好的距离，传感器和执行器模型。

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  • 来源
    《Machine learning》|1996年|266-274|共9页
  • 会议地点 Bari(IT);Bari(IT)
  • 作者

    Sven Koenig; Reid G. Simmons;

  • 作者单位

    School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3890;

    School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3890;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 计算机的应用;
  • 关键词

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