Social robots have been extensively studied in recent decades, with many researchers exploring the use of modalities such as facial expressions to achieve more natural emotions in robots. Various methods have been attempted to generate and express robot emotions, including computational models that define an affect space and show dynamic emotion changes. However, the implementation of multimodal expression in previous models is ambiguous, and the generation of emotions in response to stimuli relies on heuristic methods. In this paper, we present a framework that enables robots to naturally express their emotions in a multimodal way, where the emotion can change over time based on the given stimulus values. By representing the robot's emotion as a position in an affect space of a computational emotion model, we consider the given stimuli values as driving forces that can shift the emotion position dynamically. In order to examine the feasibility of our proposed method, a mobile robot prototype was implemented that can recognize touch and express different emotions with facial expressions and movements. The experiment demonstrated that the emotion elicited by a given stimulus is contingent upon the robot's previous state, thereby imparting the impression that the robot possesses a distinctive emotion model. Furthermore, the Godspeed survey results indicated that our model was rated significantly higher than the baseline, which did not include a computational emotion model, in terms of anthropomorphism, animacy, and perceived intelligence. Notably, the unpredictability of emotion switching contributed to a perception of greater lifelikeness, which in turn enhanced the overall interaction experience.

Social robots utilize facial expressions to convey emotions, yet most rely on predefined animation sequences for each emotion, which can result in abrupt or unnatural transitions. To address this, we employ a Control Point (CP)-based approach that dynamically adjusts facial features, enabling seamless expression transitions without predefined animations. Our study explores whether a CP-based approach enables smoother and more natural facial expressions compared to commonly used facial expression methods. Furthermore, we validate its applicability by demonstrating its effectiveness on two distinct facial designs highlighting versatility across both tested designs.

ABSTRACT Given that decision‐making typically encompasses stages such as problem recognition, the generation of alternatives, and the selection of the optimal choice, Large Language Models (LLMs) are progressively being integrated into tasks requiring the enumeration and comparative evaluation of alternatives, thereby promoting more rational decision‐making frameworks. Analysing the extent to which LLMs exhibit meaningful performance at each stage of the decision‐making process has thus become a critical area of inquiry. In particular, LLMs hold the potential to identify latent relationships within contextual information and data related to the problem domain. This capability enables them to propose novel evaluation criteria or alternatives that may otherwise be overlooked by human designers. This study seeks to advance the modelling and evaluation of the analytical hierarchy process (AHP), a widely utilised multiple criteria decision making (MCDM) method, by leveraging LLMs. To achieve this, a methodology was developed for constructing AHP models using LLMs fine‐tuned with domain‐specific documents. The performance of the proposed methodology was assessed by evaluating the extent to which its outputs aligned with reference hierarchies and criteria created by human experts under predefined AHP frameworks. Additionally, the study examined the model's efficacy in generating complete AHP hierarchies and criteria in scenarios where these were not predefined. For empirical validation, the proposed methodology was applied to assess and improve the management performance of six‐sector agricultural enterprises. Comparative analysis of the LLM‐based AHP results with human expert evaluations was conducted to determine the validity and robustness of the approach. The findings provide insights into the potential of LLMs to contribute to structured decision‐making and enhance the application of MCDM methods.

We explore the potential of mobile motion as an expression modality for home social robots with a low expressivity. To gauge the potential of mobile motion, we examine its relative efficacy in terms of emotion perception accuracy, emotion intensity, and impression (anthropomorphism and animacy) compared to screen-based facial expressions. Additionally, we explore how users perceive the emotional intensity of expressions based on different degrees of expression by manipulating emotional features such as motion size and speed. Motion expressions are less accurate than facial expressions, but perform on par with facial expressions in other metrics. Its dynamic and expressive features elicit powerful emotional conveyance, in contrast to the low emotional impact associated with the monotonous nature of screen-based facial expressions. Further research is needed on ME for specific emotions, but in general, the higher the degree of expression, the more intense the emotion conveyed. We show that the degree of expression, i.e., the combination of emotional features and their modulation, can be utilized to express the emotional intensity, situational dependence, and personality of robots. In conclusion, we argue that mobile motion is a promising method to compensate for the weaknesses of screen-based facial expression, which is the dominant expression modality for low expressivity robots.

Researchers have explored the effects of expressing emotions using various modalities in the field of social robots. Prior studies have demonstrated that the use of color in emotional expressions can enhance user acceptance, relationship building, and communication effectiveness. This study aims to validate the effectiveness of different modalities in expressing Ekman’s six basic emotions. Specifically, four modalities were compared: face expression (F), face and LED color expression (FL), face and LED color expression with blinking (FLB), and face and eye color expression (FE). To accomplish this, we developed a small social robot prototype and used a computational emotion model to improve robot dynamics and interactivity. The findings revealed that, although the F modality effectively expressed emotions, ambiguous emotions were better perceived when color or blinking was incorporated. Emotions such as anger, sadness, disgust, surprise, and fear were better conveyed to the participants when the FL, FLB, and FE modalities were utilized. For happiness, F alone was sufficient for recognition. This study provides empirical evidence on the effectiveness of different modalities for expressing emotions in social robots and offers valuable insights gathered from participants’ feedback and reflections.

This paper details the development of a Shared Indoor Smart Mobility device called AngGo. As a precursor to the development process, we conducted user research on three kinds of outdoor personal mobility. Our goal was to determine the major differences between outdoor and indoor personal mobility and to ensure that AngGo would meet the requirements of indoor personal mobility in a practical way, as informed by the results of surveys and interviews. Tests were conducted on the time-of-flight sensors to be used for indoor autonomous driving. Manual mode as well as the experiment-based equations governing the sensors were optimized through user testing. Our observational experiments, which were carried out in the lobby of a building, showed that both autonomous and manual modes functioned as designed. This study makes a contribution to the literature by describing how our AngGo device features an autonomous driving platform that can transport riders around an indoor environment.