Creating Expressive Social Robots That Convey Symbolic and Spontaneous Communication.

Robots are becoming an increasingly important part of our society and have started to be used in tasks that require communicating with humans. Communication can be decoupled in two dimensions: symbolic (information aimed to achieve a particular goal) and spontaneous (displaying the speaker's emotional and motivational state) communication. Thus, to enhance human-robot interactions, the expressions that are used have to convey both dimensions. This paper presents a method for modelling a robot's expressiveness as a combination of these two dimensions, where each of them can be generated independently. This is the first contribution of our work. The second contribution is the development of an expressiveness architecture that uses predefined multimodal expressions to convey the symbolic dimension and integrates a series of modulation strategies for conveying the robot's mood and emotions. In order to validate the performance of the proposed architecture, the last contribution is a series of experiments that aim to study the effect that the addition of the spontaneous dimension of communication and its fusion with the symbolic dimension has on how people perceive a social robot. Our results show that the modulation strategies improve the users' perception and can convey a recognizable affective state.

Modelling Multimodal Dialogues for Social Robots Using Communicative Acts.

Social Robots need to communicate in a way that feels natural to humans if they are to effectively bond with the users and provide an engaging interaction. Inline with this natural, effective communication, robots need to perceive and manage multimodal information, both as input and output, and respond accordingly. Consequently, dialogue design is a key factor in creating an engaging multimodal interaction. These dialogues need to be flexible enough to adapt to unforeseen circumstances that arise during the conversation but should also be easy to create, so the development of new applications gets simpler. In this work, we present our approach to dialogue modelling based on basic atomic interaction units called Communicative Acts. They manage basic interactions considering who has the initiative (the robot or the user), and what is his/her intention. The two possible intentions are either ask for information or give information. In addition, because we focus on one-to-one interactions, the initiative can only be taken by the robot or the user. Communicative Acts can be parametrised and combined in a hierarchical manner to fulfil the needs of the robot's applications, and they have been equipped with built-in functionalities that are in charge of low-level communication tasks. These tasks include communication error handling, turn-taking or user disengagement. This system has been integrated in Mini, a social robot that has been created to assist older adults with cognitive impairment. In a case of use, we demonstrate the operation of our system as well as its performance in real human-robot interactions.