Learning from History for Behavior-Based Mobile Robots in Non-Stationary Conditions
Learning from History for Behavior-Based Mobile Robots in Non-Stationary Conditions
复制标题
非静止条件下基于行为的移动机器人的历史学习
DOI：
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复制
发表时间：
1998
期刊：
影响因子：
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通讯作者：
M. Matarić
中科院分区：
文献类型：
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作者：
F. Michaud;M. Matarić
在移动机器人领域学习是一项非常具有挑战性的任务，特别是在非平稳条件下。基于行为的方法已被证明在使移动机器人在现实世界中工作方面是有用的。由于行为负责管理机器人与其环境之间的交互，因此可以利用观察它们的使用来对这些交互进行建模。在我们的方法中，机器人最初被给予一组“行为产生”模块供其选择，算法提供了一种基于记忆的方法，根据它们的使用历史动态地适应这些行为的选择。在多机器人觅食任务的背景下，使用基于视觉和声纳的先锋I机器人在非静止条件下验证了该方法。结果表明，该方法可以有效地利用世界上所经历的任何规律，从而为学习机器人带来快速和适应性的专业化。
Learning in the mobile robot domain is a very challenging task, especially in non-stationary conditions. The behavior-based approach has proven to be useful in making mobile robots work in real-world situations. Since the behaviors are responsible for managing the interactions between the robots and its environment, observing their use can be exploited to model these interactions. In our approach, the robot is initially given a set of “behavior-producing” modules to choose from, and the algorithm provides a memory-based approach to dynamically adapt the selection of these behaviors according to the history of their use. The approach is validated using a vision- and sonar-based Pioneer I robot in non-stationary conditions, in the context of a multi-robot foraging task. Results show the effectiveness of the approach in taking advantage of any regularities experienced in the world, leading to fas t and adaptable specialization for the learning robot.