Task-oriented dialogue system has a vital role in Human-Robot Interaction (HRI). However, it has been developed based on conventional pipeline approach which has several drawbacks; expensive, time-consuming, and so on. Based on this approach, developers manually define a robot's behaviour such as gestures and facial expressions on the corresponding dialogue states. Recently, end-to-end learning of Recurrent Neural Networks (RNNs) is an attractive solution for the dialogue system. In this paper, we proposed a social robot system using end-to-end dialogue system in the context of hospital receptionist. We utilized Hybrid Code Network (HCN) as an end-to-end dialogue system and extended to select both response and gesture using RNN based gesture selector. We evaluate its performance with human users and compare the results with one of the conventional methods. Empirical result shows that the proposed method has benefits in terms of dialogue efficiency, which indicates how efficient users were in performing the given tasks with the help of the robot. Moreover, we achieved the same performance regarding the robot's gesture with the proposed method compared to manually defined gestures.

A robot cannot be warm and friendly - or can it? To explore whether a robot can be a medical receptionist, we developed a robotic system for interacting with patients at a doctor's clinic, including acting friendly. We designed the robot to interact naturally with patients at the start and finish of a clinic visit. We investigated people's perceptions to the robot in a wizard-of-Oz study, where the participants interacted with the robot over four interactions. 40 participants evaluated the robot. The results indicate the participants thought the robot could be a friendly receptionist, especially after repeated interactions with the robot. However, the participants mainly thought the robot was friendly in a “professional” way, rather than a personal friend.

This paper presents a new version of robot receptionist system for healthcare facility environment. Our HealthBots consists of three subsystems: a receptionist robot system, a nurse assistant robot system, and a medical server. Our first version of receptionist robot, interacts with human at hospital reception, gives instructions to human verbally, but cannot understand what human says, so it uses a touch screen to get the response from human. In this paper, we design a receptionist robot that recognizes human face as well as speech, which enhances verbal interaction skill of robot. In addition, we design a reaction generation engine to generate appropriate reactive motions and speech. Moreover, we study which social skills are important to a hospital receptionist robot to enhance social interaction, such as friendliness and attention. We implemented perception modules, decision-making modules, and reaction modules to our HealthBots architecture, and did two case studies to find essential social skills for hospital receptionist robots.

In this paper, we analyze lip-sync factors affecting communication of android robots. We have generated eight mouth shapes for single vowels based on the Hangul vowel triangle chart. We assume that the mouth shape and lip-sync timing are important factors for communication with android robots. An experiment was conducted in a noisy environment during an exhibition. A total of 120 people were surveyed about how well they understood and why. Unlike the hypothesis, sound is found to be an important factor that affects human understanding. Because experimental results can be affected by noise and whether subjects have seen a robot or not, we will plan to conduct further experiments in a quiet environment after explaining android robots to subjects sufficiently.

This paper presents a process to build a classifier in a data-driven way for recognizing engagement of children in a robot-based math quiz game. The process consists of collecting video recordings from HRI experiments; annotating the social signals and engagement states via video analysis; extracting feature vectors from the annotations and training classifiers. We conducted an experiment with 7 participants of 10 -- 11 years of age using an android robot EveR-4. With three coders annotating the video recordings and extracting features by snapshot model with 1-second time window, we achieved 84.83% recall performance.