Abstract
Developing dialogue services for robots has been promoted nowadays for providing natural human–robot interactions to enhance user experiences. In this study, we adopted a service-oriented framework to develop emotion-aware dialogues for service robots. Considering the importance of the contexts and contents of dialogues in delivering robot services, our framework employed deep learning methods to develop emotion classifiers and two types of dialogue models of dialogue services. In the first type of dialogue service, the robot works as a consultant, able to provide domain-specific knowledge to users. We trained different neural models for mapping questions and answering sentences, tracking the human emotion during the human–robot dialogue, and using the emotion information to decide the responses. In the second type of dialogue service, the robot continuously asks the user questions related to a task with a specific goal, tracks the user’s intention through the interactions and provides suggestions accordingly. A series of experiments and performance comparisons were conducted to evaluate the major components of the presented framework and the results showed the promise of our approach.
Highlights
Researchers and engineers have been building service robots that can interact with people and achieve given tasks
To enhance the service performance and equip the robot with social competences, in this work, we developed an emotion-aware human–robot dialogue framework extended from our previous research presented in [7], with a series of additional experiments and newly developed dialogue services
In addition to the above domain-specific dialogue modeling, this section presents the task-oriented subsystem we developed for the service robot to achieve practical applications with specific goals
Summary
Researchers and engineers have been building service robots that can interact with people and achieve given tasks. The services are mostly laboring services, in which robots take actions in the physical environment to assist people. Robots are expected to play more important roles in providing domain-specific knowledge services and task-oriented services. To deliver these services, robots communicate with users through a natural way of spoken language because conversation is a key instrument for developing and maintaining mutual relationships. Following our previous studies that adopted a service-oriented architecture to develop action-oriented robot services, in this work we presented a trainable framework for modeling emotion-aware human–robot dialogues to provide the aforementioned services
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