Toward Open Knowledge Enabling for Human- Robot Interaction

Xiaoping Chen, Jiongkun Xie, Jianmin Ji, Zhiqiang Sui


This paper presents an effort to enable robots to utilize open-source knowledge resources au- tonomously for human-robot interaction. The main challenges include how to extract knowledge in semi-structured and unstructured natural languages, how to make use of multiple types of knowl- edge in decision making, and how to identify the knowledge that is missing. A set of techniques for multi-mode natural language processing, integrated decision making, and open knowledge search- ing is proposed. The OK-KeJia robot prototype is implemented and evaluated, with special attention to two tests on 11,615 user tasks and 467 user desires. The experiments show that the overall per- formance improves remarkably due to the use of appropriate open knowledge.


Human-robot interaction, open knowledge, NLP, decision-making, social robotics

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