Developing Context-Aware Dialoguing Services for a Cloud-Based Robotic System

Abstract

In the development of service robots, building dialoguing services for robots to provide natural human-robot interactions and enhancing user experiences is now advocated. In this type of service, a robot can play the role of a knowledgeable consultant and deliver domain-specific knowledge to end users. Following our previous studies in constructing action-oriented robot services, in this paper, we adopt a service-oriented framework to develop a context-aware dialoguing service for a cloud robot. Our work has several unique features: it trains a deep neural model to generate answers in response to the users' questions, utilizes an external knowledge resource to further enrich knowledge for searching for answers, constructs a reasoning procedure to exploit the answers of similar questions, and most importantly, and develops an integrated approach that embeds both contextual information and dialoguing content in the same model. To evaluate the proposed approach, we conducted a series of experiments and implemented several strategies for performance comparison. The results confirmed the usefulness and effectiveness of the integrated context and content approach.

Keywords

• Context awareness
• emotion
• human-robot dialogue
• neural learning
• service robot