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Integrating Robot Task Scripts with a Cognitive Architecture for Cognitive Human-Robot Interactions

机译:将机器人任务脚本与用于认知人机交互的认知架构相集成

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In spite of the long history of intelligent humanoid robots, endowing human-like commonsense knowledge to a robot is still a difficult problem. Since a knowledge base consisting of a large number of rules and facts is not an efficient structure that can express situational and background knowledge for humanoid robots, more compact yet forgiving representation is required. Our proposal is to employ a script design that contains richness and diversity needed for robot''s task planning, in conjunction with a robust cognitive architecture called EM-ONE, the latest extant account of an implemented cognitive architecture. The script structure has its advantages in flexibility and extensibility for a variety of situations or tasks at hand, along with reusability. As the number of scripts increases, the coverage for diverseness of Human-Robot Interaction (HRI) situation grows. In this paper, we discuss three cognitive models used as our cognitive architecture basis and describe our efforts for generating task scripts in a semi-automatic way by reusing the already existing scripts.
机译:尽管智能类人机器人的历史悠久,但将类人常识知识赋予机器人仍然是一个难题。由于由大量规则和事实组成的知识库不是可以表达类人机器人的情境和背景知识的有效结构,因此需要更紧凑但可以宽容的表示形式。我们的建议是采用一种脚本设计,其中包含机器人任务计划所需的丰富性和多样性,并结合一个强大的认知架构EM-ONE,这是现有认知架构的最新描述。脚本结构在针对各种情况或任务的灵活性和可扩展性以及可重用性方面具有优势。随着脚本数量的增加,人机交互(HRI)情况多样化的覆盖范围也随之增加。在本文中,我们讨论了用作认知体系结构基础的三种认知模型,并描述了我们通过重用已经存在的脚本以半自动方式生成任务脚本的工作。

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