Learning over Subgoals for Efficient Navigation of Structured, Unknown Environments
Learning over Subgoals for Efficient Navigation of Structured, Unknown Environments
复制标题
学习子目标以有效导航结构化的未知环境
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
N. Roy
中科院分区：
文献类型：
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作者：
Gregory J. Stein;Christopher Bradley;N. Roy
我们提出了一种新的技术，通过学习预测未知空间的属性，在长时间内有效地导航未知环境。我们生成一个由当前地图定义的动态动作集，根据这些动作对贝尔曼方程进行因子分解，并估计术语，例如导航超出特定子目标将导致死胡同的概率，否则很难计算。与依赖于Dijkstra算法的标准乐观规划技术相比，在真实世界的平面图中使用我们的学习子目标规划器进行导航的模拟代理显示出21%的预期成本降低，并且真实世界的代理也显示出有希望的导航性能。
We propose a novel technique for efficiently navigating unknown environments over long horizons by learning to predict properties of unknown space. We generate a dynamic action set defined by the current map, factor the Bellman Equation in terms of these actions, and estimate terms, such as the probability that navigating beyond a particular subgoal will lead to a dead-end, that are otherwise difficult to compute. Simulated agents navigating with our Learned Subgoal Planner in real-world floor plans demonstrate a 21% expected decrease in cost-to-go compared to standard optimistic planning techniques that rely on Dijkstra’s algorithm, and real-world agents show promising navigation performance as well.
影响因子：
3.5
作者：
Hornung, Armin;Wurm, Kai M.;Burgard, Wolfram
通讯作者：
Burgard, Wolfram