张宁 Learning Motion Planning Policies in Uncertain Environments through Repeated Task Executions
通过重复任务执行学习不确定环境中的运动规划策略
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Florence Tsang, Ryan A. Macdonald, and Stephen L. Smith
The ability to navigate uncertain environments from a start to a goal location is a necessity in many applications. While there are many reactive algorithms for online replanning, there has not been much investigation in leveraging past executions of the same navigation task to improve future executions. In this work, we first formalize this problem by introducing the Learned Reactive Planning Problem (LRPP). Second, we propose a method to capture these past executions and from that determine a motion policy to handle obstacles that the robot has seen before. Third, we show from our experiments that using this policy can significantly reduce the execution cost over just using reactive algorithms.
在许多应用中，从开始到目标位置导航不确定环境的能力是必需的。尽管有许多用于在线重新计划的反应算法，但是在利用相同导航任务的过去执行来改进将来的执行方面没有太多调查。在这项工作中，我们首先通过引入学习反应规划问题（LRPP）来正式化这个问题。 其次，我们提出了一种方法来捕获这些过去的执行，并从中确定一个运动策略来处理机器人以前看到的障碍。 第三，我们从实验中可以看出，使用这种策略可以显着降低执行成本，而不仅仅是使用反应算法。