Yan Fu, Shiqi Li, Kan Qiu, Xue Li, L. Chen, Jie Tan
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引用次数: 2
Abstract
It is natural and efficient to use natural language for transferring knowledge from a human to a robot. The inconsistency of human-robot spatial cognitive style, the high frequency communication and low-cognition-level symbol matching control have greatly affected the operational efficiency in spatial-cognition-demanding tasks such as positioning and exploring. To fill the knowledge gap, this study applies ACT-R cognitive theory to establish a new way of knowledge representation and processing for the robots with a purpose to improve the flexibility of natural language facilitated humanrobot cooperation. This idea is specifically validated in the task of human-robot teaming space exploration.
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