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通过工具构建进行创造性问题解决的特征引导搜索

Feature Guided Search for Creative Problem Solving Through Tool Construction.

作者信息

Nair Lakshmi, Chernova Sonia

机构信息

Georgia Institute of Technology, Atlanta, GA, United States.

出版信息

Front Robot AI. 2020 Dec 23;7:592382. doi: 10.3389/frobt.2020.592382. eCollection 2020.

Abstract

Robots in the real world should be able to adapt to unforeseen circumstances. Particularly in the context of tool use, robots may not have access to the tools they need for completing a task. In this paper, we focus on the problem of tool construction in the context of task planning. We seek to enable robots to construct replacements for missing tools using available objects, in order to complete the given task. We introduce the Feature Guided Search (FGS) algorithm that enables the application of existing heuristic search approaches in the context of task planning, to perform tool construction efficiently. FGS accounts for physical attributes of objects (e.g., shape, material) during the search for a valid task plan. Our results demonstrate that FGS significantly reduces the search effort over standard heuristic search approaches by ≈93% for tool construction.

摘要

现实世界中的机器人应该能够适应意外情况。特别是在工具使用的情况下,机器人可能无法获得完成任务所需的工具。在本文中,我们关注任务规划背景下的工具构建问题。我们试图使机器人能够使用可用物体构建缺失工具的替代品,以完成给定任务。我们引入了特征引导搜索(FGS)算法,该算法能够在任务规划背景下应用现有的启发式搜索方法,以高效地执行工具构建。FGS在搜索有效任务计划时考虑物体的物理属性(例如形状、材料)。我们的结果表明,对于工具构建,FGS比标准启发式搜索方法显著减少了约93%的搜索工作量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7798/7806064/08897ef06420/frobt-07-592382-g0001.jpg

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