To perform tasks well in a new domain, one must first know something about it. This paper reports on a robot controller for navigation through unfamiliar indoor worlds. Based on spatial affordances, it integrates planning with reactive heuristics. Before it addresses specific targets, however, the system deliberately explores for high-level connectivity and captures that data in a cognitive spatial model. Despite limited exploration time, planning in the resultant model is faster and better supports successful travel in a challenging, realistic space.

中文翻译：

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深思熟虑的探索支持在陌生世界中的导航

要在一个新领域中出色地执行任务，首先必须对其有所了解。本文报告了一种用于在陌生的室内世界中导航的机器人控制器。基于空间可供性，它将规划与反应启发式相结合。然而，在解决特定目标之前，系统会特意探索高级连接并在认知空间模型中捕获该数据。尽管探索时间有限，但最终模型中的规划更快，更好地支持在具有挑战性的现实空间中成功旅行。