泡泡一分鐘：Learning Motion Planning Policies in Uncertain Environments through Repeated Task Executions

張寧  Learning Motion Planning Policies in Uncertain Environments through Repeated Task Executions

經過重複任務執行學習不肯定環境中的運動規劃策略
連接：https://pan.baidu.com/s/1TlSJn0fXuKEwZ9vts4xA6g
提取碼：jwsd
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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.

在許多應用中，從開始到目標位置導航不肯定環境的能力是必需的。儘管有許多用於在線從新計劃的反應算法，可是在利用相同導航任務的過去執行來改進未來的執行方面沒有太多調查。在這項工做中，咱們首先經過引入學習反應規劃問題（LRPP）來正式化這個問題。 其次，咱們提出了一種方法來捕獲這些過去的執行，並從中肯定一個運動策略來處理機器人之前看到的障礙。 第三，咱們從實驗中能夠看出，使用這種策略能夠顯着下降執行成本，而不單單是使用反應算法。