Abstract
Augmented reality, augmented television and second screen are cutting edge technologies that provide end users extra and enhanced information related to certain events in real time. This enriched information helps users better understand such events, at the same time providing a more satisfactory experience. In the present paper, we apply this main idea to human–robot interaction (HRI), to how users and robots interchange information. The ultimate goal of this paper is to improve the quality of HRI, developing a new dialog manager system that incorporates enriched information from the semantic web. This work presents the augmented robotic dialog system (ARDS), which uses natural language understanding mechanisms to provide two features: (i) a non-grammar multimodal input (verbal and/or written) text; and (ii) a contextualization of the information conveyed in the interaction. This contextualization is achieved by information enrichment techniques that link the extracted information from the dialog with extra information about the world available in semantic knowledge bases. This enriched or contextualized information (information enrichment, semantic enhancement or contextualized information are used interchangeably in the rest of this paper) offers many possibilities in terms of HRI. For instance, it can enhance the robot's pro-activeness during a human–robot dialog (the enriched information can be used to propose new topics during the dialog, while ensuring a coherent interaction). Another possibility is to display additional multimedia content related to the enriched information on a visual device. This paper describes the ARDS and shows a proof of concept of its applications.
Highlights
The area of human–robot interaction (HRI) is devoted to investigating the relations between robots and humans and how they communicate
The most popular input mode is the voice, which is processed by automatic speech recognition systems (ASR), and the most popular output mode is a verbal robot utterance, usually generated by a voice synthesizer (text-to-speech system (TtS))
In order to deal with this problem, we have developed a local cache memory that stores the enhanced information related to each entity the first time it is processed
Summary
The area of human–robot interaction (HRI) is devoted to investigating the relations between robots and humans and how they communicate. Dialog systems (in the context of this paper, we consider dialogs as a bidirectional flow of messages or information, using many possible communicative modes, such as verbal and non-verbal language, between two or more agents; “dialog” and “interaction systems” might be considered equivalent terms) began to consider several ways or channels to share a message between interactors [1,2,3,4,5]. These channels are called modalities, or modes. This information corresponds to the discourse level of the natural language layers
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
