This paper presents an effort to enable robots to utilize open-source knowledge resources autonomously 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 knowledge 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 searching 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 performance improves remarkably due to the use of appropriate open knowledge.
As more and more open knowledge sources become available, it is interesting to explore opportunities of enhancing autonomous agents' capacities by utilizing the knowledge in these sources, instead of hand-coding knowledge for agents. A major challenge towards this goal lies in the translation of the open knowledge organized in multiple modes, unstructured or semi-structured, into the internal representations of agents. In this paper we present a set of multimode NLP techniques to formalize the open knowledge for autonomous agents. Two case studies are reported in which our robot KeJia, equipped with the multi-mode NLP techniques, succeeded in acquiring knowledge from the microwave oven manual and from the open knowledge database, OMICS, and solving problems that could not be solved before the robot acquired the knowledge.
Abstract. This paper reports a series of simulation competitions on domestic robots. All of these five competitions were based on a simulation platform focused on evaluating high-level functions of a domestic robot, including task planning and dialogue understanding. The object of holding these competitions is to promote research and development of service robots while avoiding limitations imposed by hardware of real robots. We also analyze the results and performances of participating teams since the competition was first held in 2009, showing that more and more terms are participating and they are performing better and better.
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