The global housing crisis has led to the widespread adoption of micro-apartments, which present significant challenges due to their limited space. To address these issues, we developed oOoBOT, a modular robotic furniture system capable of transforming its form and function to meet diverse user needs. The design incorporates a Hoberman mechanism for expandable structures and an umbrella-inspired tabletop for flexible use. Enhanced mobility and reconfigurability are achieved through modular components. By optimizing space efficiency, oOoBOT enables multifunctional activities such as sleeping, working, and socializing, offering a practical solution for confined living environments.

With advancements in Large Language Models, conversational companion robots are increasingly recognized for their potential in supporting and caring for older adults. This study examines older adults’ experiences and functional preferences for a conversational robot designed to enable personalized interactions across multiple sessions. The robot engages users in open-ended conversations by recalling previous dialogues, integrating personal information, and facilitating memory-stimulating conversations. Thirty healthy older adults participated in three one-hour interaction sessions with the robot, followed by post-interaction surveys. Results showed that participants found the interactions engaging and meaningful, expressing high satisfaction with the robot’s ability to remember them and sustain personalized conversations. Among the robot’s functions, health monitoring and retrospective memory support were identified as highly valued by older adults. These findings provide insights for developing more adaptive and socially engaging robots, emphasizing the importance of personalized memory retention and user-tailored interactions in enhancing social support and cognitive engagement for older adults.

Isolation wards operate in quarantine rooms to prevent cross-contamination caused by infectious diseases. Behind the benefits, medical personnel can have the infection risk from patients and the heavy workload due to the isolation. This work proposes a robot-assisted system to alleviate these problems in isolation wards. We conducted a survey about the medical staff's difficulties and envisioning robots. Using the investigation result, we devised three valuable services using two kinds of heterogeneous robots: telemedicine, emergency alert, and delivery services by care robots and delivery robots. Our system also provides user-interactive components such as a dashboard for medical staff and a patient app for inpatients. To manage the services efficiently, we suggest the robotic system based on a central control server and a hierarchical management architecture. Through a user study, we reviewed the usability of the developed system and its future directions.

Voice cloning techniques using deep neural networks have been used for frauds, such as false financial transactions or fake news, limiting their widespread application. However, when incorporated with communication robots, voice cloning may increase social connectedness for isolated people. Herein, we suggested the concept of a voice-cloned communication robot (VCR), equipped with acquaintances’ voices to allow people in isolation to feel connected with their acquaintances. We developed a prototype VCR, conducted exploratory qualitative and quantitative studies, and verified its potential effectiveness in enhancing the mental health of isolated people.

To help people use their limited space efficiently, we propose an origami-based transformable robotic space called “PopupBot.” Specifically, we developed a robotic playhouse for children. A large origami structure with a bellows pattern is controlled by a servo motor and transforms into various types of furniture. For natural child-robotic space interaction, PopupBot perceives the child's intention for the space through speech recognition and provides appropriate space by inferring the space type matching the intention. We expect the PopupBot to provide a new space for people who suffer from staying in limited space especially due to the COVID-19 pandemic.

How do native English speakers and native Korean speakers politely make a request to a robot? Previous human-robot interaction studies on English have demonstrated that humans use indirect speech acts (ISAs) frequently to robots to make their requests polite. However, it is unknown whether humans considerably used ISAs to robots in other languages. In addition to ISAs, Korean has other politeness expressions called honorifics, which indicate different politeness from that of ISAs. This study aimed to investigate the cultural differences in humans' politeness expressions and politeness when they make requests to robots and to re-examine the effect of conventionality of context on the use of politeness expressions. We conducted a replication experiment of Williams et al. (2018) on native Korean speakers and analyzed their use of ISAs and honorifics. Our results showed that ISAs are rarely used in task-based human-robot interaction in Korean. Instead, honorifics are more frequently used than ISAs and are more common in conventionalized contexts than in unconventionalized contexts. These results suggest that the difference in politeness expressions and politeness between English and Korean exist in both human-robot interaction and human-human interaction. Furthermore, the conventionality of context has a strong constraint on making humans follow social norms in both languages.

Social relations within a group are one of the factors that build the social context. Less attention has been paid to social relations within a group for human-robot interaction design. In this study, we designed different types of service behaviors for social relations in a mixed age group and conducted an experiment to investigate the effect of service behavior types (serving the elderly first versus serving the young first versus serving without priority) on a user's evaluation of a service robot. We only found that service evaluation and appropriateness of the robot were rated less positively under the condition of serving the young first than under other conditions. In addition, we found that the participants evaluated the robot serving the young first as impolite. The effect of the robot's behaviors on service evaluation and appropriateness was mediated by politeness. The current study provides an initial basis for human-robot interaction design for social norms in group-robot interaction.

A non-lexical backchannel is known to be dependent on the conversational context, and its form can be distinguished by the social relation between the speaker and the listener in the Korean language. Thus, to investigate the effect of a non-lexical backchannel, we conducted a 2 (context: information-centric versus emotion-centric) × 3 (forms of backchannel: "ne" versus "eo" versus "eum") mixed-participant experiment (N = 96). After watching video stimuli, the participants evaluated a robot using one of the three forms of non-lexical backchannels under both contexts. No significant main effect of the forms of the non-lexical backchannel was found. By contrast, we found that task attraction and appropriateness of the robot were rated more positively under the information-centric context than under the emotion-centric context. The functions of the non-lexical backchannel of attentive listening and understanding were manifested more under the information-centric context than under the emotion-centric context. Furthermore, these functions mediated the effect of conversational context on the user perception of the robot in terms of task attraction and appropriateness.

Verbal interaction between a human and a robot may play a key role in conveying suitable directions for a robot to achieve the goal of a user's request. However, a robot may need to correct task plans or make new decisions with human help, which would make the interaction inconvenient and also increase the interaction time. In this paper, we propose a new verbal interaction-based method that can generate plan models and execute proper actions without human involvement in the middle of performing a task by a robot. To understand the verbal behaviors of humans when giving instructions to a robot, we first conducted a brief user study and found that a human user does not explicitly express the required task. To handle such unclear instructions by a human, we propose two different algorithms that can generate a component of new plan models based on intents and entities parsed from natural language and can resolve the unclear entities existed in human instructions. An experimental scenario with a robot, Cozmo, was tried in the lab environment to test whether or not the proposed method could generate an appropriate plan model. As a result, we found that the robot could successfully accomplish the task following human instructions and also found that the number of interactions and components in the plan model could be reduced as opposed to the general reactive plan model. In the future, we are going to improve the automated process of generating plan models and apply various scenarios under different service environments and robots.

Service robots with social intelligence are starting to be integrated into our everyday lives. The robots are intended to help improve aspects of quality of life as well as improve efficiency. We are organizing an exciting workshop at HRI 2019 that is oriented towards sharing the ideas amongst participants with diverse backgrounds ranging from Human-Robot Interaction design, social intelligence, decision making, social psychology and aspects and robotic social skills. The purpose of this workshop is to explore how social robots can interact with humans socially and facilitate the integration of social robots into our daily lives. This workshop focuses on three social aspects of human-robot interaction: (1) technical implementation of social robots and products, (2) form, function and behavior, and (3) human behavior and expectations as a means to understand the social aspects of interacting with these robots and products.

Human has an ability to adjust utterance depending on the state of interlocutor. In this study, we explore the verbal behaviors of human through interaction with two smart speakers that have different level of task competence. We analyzed (1) linguistic behaviors appeared in user's utterance, (2) length of the uttered speech, and (3) required pragmatics skills to understand the user's intent. As a result, there were no significant difference in linguistic behaviors and length of the speech while user interacts with speakers with different task competence. In addition, various pragmatics elements were equally utilized and especially, implied intentions were frequently observed in user's short utterance even under simple interaction scenarios.

In this paper, we introduce an ontology-based home care service robot that can provide personalized care for people who are in the early stage of dementia. The hardware and software framework encompassed in the proposed service robot was developed to carry out care services in their daily life at home. Specifically, to generate adaptive task plans in diverse caring situation, context reasoner and ontological model of dementia are included. Ontology includes various concepts that are related with the knowledge of caring dementia patient: dementia, dementia symptom, environment of around patient, and situation during patient's daily life. To evaluate if the proposed service robot could provide appropriate care service or not, experimental care scenario for helping a person with dementia take medicine was tried in the lab environment. Although tasks of the robot required for the experiment are rather simple, we have demonstrated that the robot could provide a personalized service that may be beneficial to dementia patient, family members and caregivers. In the future, we will add more care knowledge in the ontology and further develop a variety of care services. Additionally, we are going to test the care service robot in a real environment with actual dementia patient.

In this study, we propose a probabilistic approach to overcome time delay for a mobile robot. The robot receives sporadic user input when using brain-computer interface, and related studies have been considered about various strategies to control the robot as intended. However, the most of experiments heavily depended on the driving environment because of the low information transfer rate. To solve this problem, we propose a probabilistic local goal determination method. Our approach accumulates user input sequence, and specifies it as a feasible path. Simulations in the room and on the track are provided. As a result, we show that the proposed method is robust to long time delay, and achieves the mission with fewer inputs.

Many signals are naturally described by continuous contours in the time-frequency plane, but standard time-frequency methods disassociate continuous structures into isolated “atoms” of energy. Here we propose a method that represents any discrete time-series as a set of time-frequency contours. The edges of the contours are defined by fixed points of a generalized reassignment algorithm. These edges are linked together by continuity such that each contour represents a single phase-coherent region of the time-frequency plane. By analyzing the signal across many time-scales, an over-complete set of contours is generated, and from this redundant set of shapes the simplest, most parsimonious forms may be selected. The result is an adaptive time-frequency analysis that can emphasize the continuity of long-range structure. The proposed method is demonstrated with a few examples.

In this paper, data association method using audio and video data together to localize targets in a cluttered environment and detect who is speaking to the robot is presented. Particle filter is applied to find an optimal association between target and measurements efficiently. State variables are composed of position and speaking status. To update the speaking state, we first evaluate the upcoming sound signal based on the cross-correlation between microphones and then calculate the likelihood for audio information. Visual measurement is applied to find an optimal association between target and visual observations. The number of targets that robot should interact is updated based on the vision and audio information. Several experimental data were collected beforehand and simulated on computer to verify the performance of proposed data association method for speaker selection problem in a cluttered environment.

In this paper, a single speaker tracking mechanism with particle filter is presented for a reliable interaction with robot. To have a smooth tracking result, we use not only audio data but also video data. For sound observation, we use Time Delay Of Arrival (TDOA) between two microphones, and in order to expect high reliability in the likelihood of localization, we apply variable sensitivity values according to the angle of arrival for a triangle arrangement of microphones to calculate the likelihood for audio observation. For video observation, we use face detection routine of OpenCV which is widely available through the Internet. Together with audio and video data, we found out that more reliable source tracking is possible if we apply data fusion mechanism at every possible environment on a robot platform than single information.