A robot finds it really hard to learn creatively and adapt to new unseen challenges. This is mainly because of the minimal information it has access to or experience towards. Paulius et al. [1] presented a way to construct functional graphs that encapsulate. Sakib et al. [2] further expanded FOON objects for robotic cooking. This paper presents a comparative study of Breadth First Search (BFS), Greedy Breadth First search (GBFS) with two heuristic functions, and Iterative Depth First Search (IDFS) and provides a comparison of their performance.
翻译：机器人发现很难创造性地学习并适应新的不可见的挑战,这主要是因为它能够获得的或经历的信息极少。Paulius等人[1] 介绍了构建功能图的方法,这些功能图包含Sakib 等人[2] 进一步扩展FOON天体用于机器人烹饪。本文对Breadth First Search(BFS)、Greedy Breadth First Search(GBFS)和具有两种超常功能的超常深度第一次搜索(IDFS)进行了比较研究,并比较了它们的性能。
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