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.
To improve generality and scalability, researchers have tried to exploit online knowledge and learn large-scare knowledge representations to build a general-purpose system for instruction understanding. For example, Lemaignan @cite_28 @cite_19 have tried to understand and reason about knowledge around an action model using online knowledge for robots. It is worth pointing out that we previously proposed an integrated system @cite_24 for our KeJia robot consisting of multi-mode NLP, integrated decision-making, and open knowledge searching.
@cite_28 @cite_19 @cite_24
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This paper presents how extraction, representation and use of symbolic knowledge from real-world perception and human-robot verbal and non-verbal interaction can actually enable a grounded and shared model of the world that is suitable for later high-level tasks such as dialogue understanding. We show how the anchoring process itself relies on the situated nature of human-robot interactions. We present an integrated approach, including a specialized symbolic knowledge representation system based on Description Logics, and case studies on several robotic platforms that demonstrate these cognitive capabilities. Users may ask a service robot to accomplish various tasks so that the designer of the robot cannot program each of the tasks beforehand. As more and more open-source knowledge resources become available, it is worthwhile trying to make use of open-source knowledge resources for service robots. The challenge lies in the autonomous identification, acquisition and utilization of missing knowledge about a user task at hand. In this paper, the core problem is formalized and the complexity results of the main reasoning issues are provided. A mechanism for task planning with open-knowledge rules which are provided by non-experts in semi-structured natural language and thus generally underspecified are introduced. Techniques for translating the semi-structured knowledge from a large open-source knowledge base are also presented. Experiments showed a remarkable improvement of the system performance on a test set consisting of hundreds of user desires from the open-source knowledge base.