A robot finds it really hard to learn creatively and adapt to the new unseen challenges. This is mainly because of the very limited information it has access or experience towards. Paulius et al. \cite{b4} presented a way to construct functional graphs which can encapsulate. Sakib et al. \cite{b1} 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 the 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。 这主要是因为它获得的信息或经验非常有限。 Paulius 等人(\cite{b4}) 介绍了一种构建功能图的方法,可以包装Sakib等人(akib等人(cite{b1}) 进一步扩大FOON天体用于机器人烹饪。 本文对Breadth First Search(BFS) 、 含有两种超常功能的贪婪Breadth First(GBFS) 和 循环深度第一次搜索(IDFS) 进行了比较研究, 并比较了它们的性能。