Multi-mode Natural Language Processing for human-robot interaction

Xie, Jiongkun; Chen, Xiaoping; Ji, Jianmin

doi:10.3233/web-150325

Cited by 3 publications

(1 citation statement)

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“…Later on, [27], [28], [29] presented a system called KNOWROB for processing knowledge in order to achieve more flexible and general behavior. Most recently, we proposed a formal description of knowledge gaps between user instructions and local knowledge in robotic system for instruction understanding [30], [8], [31], [32]. However, in these efforts using OMICS for robot task planning with user instructions, common verbs are normally not defined in the knowledge base, which limits their performance on utilizing existing open knowledge.…”

Section: Case Study On Kejia Robotmentioning

confidence: 99%

Understanding User Instructions by Utilizing Open Knowledge for Service Robots

Lu,

Wu,

Chen

2016

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Understanding user instructions in natural language is an active research topic in AI and robotics. Typically, natural user instructions are high-level and can be reduced into low-level tasks expressed in common verbs (e.g., 'take', 'get', 'put'). For robots understanding such instructions, one of the key challenges is to process high-level user instructions and achieve the specified tasks with robots' primitive actions. To address this, we propose novel algorithms by utilizing semantic roles of common verbs defined in semantic dictionaries and integrating multiple open knowledge to generate task plans. Specifically, we present a new method for matching and recovering semantics of user instructions and a novel task planner that exploits functional knowledge of robot's action model. To verify and evaluate our approach, we implemented a prototype system using knowledge from several open resources. Experiments on our system confirmed the correctness and efficiency of our algorithms. Notably, our system has been deployed in the KeJia robot, which participated the annual RoboCup@Home competitions in the past three years and achieved encouragingly high scores in the benchmark tests.

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