Abstract
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.
Highlights
The field of social robotics has attracted a lot of interest in recent years and has begun to become a part of our daily lives
First, we show how the CAs are applied in different interactions; second, we measure the response time of our system in order to conduct natural human–robot interactions; and, third, we show the utility of the built-in recovery mechanisms
One example of agent-based DS is the work presented by Lee et al [21], who proposed an example-based dialogue modelling approach for multi-domain DSs where the Dialogue Manager (DM) generates a structured query language statement based on the dialogue history and the current dialogue frame
Summary
The field of social robotics has attracted a lot of interest in recent years and has begun to become a part of our daily lives. Dialogues have to be fast enough to achieve the response times required for a natural interaction Considering these issues, developing new robotic applications, where human–robot interaction is involved, is a difficult and time consuming task. CAs provide several built-in recovery mechanisms to handle frequent problems that can arise during any dialogue (e.g., not obtaining a response from the other peer, communication errors due to perception problems and unexpected changes of topic during the conversation) This approach will ease the modelling of human–robot interactions by freeing up time from low-level interaction task development and allowing to focus on the combination and parametrisation of the CAs and CCAs. Our social robots are designed for healthcare applications where the robot interacts with older adults that have mild levels of cognitive impairment.
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