Using an Android Robot to Improve Social Connectedness by Sharing Recent Experiences of Group Members in Human-Robot Conversations

Fu, Changzeng; Liu, Chaoran; Ishi, Carlos Toshinori; Yoshikawa, Yuichiro; Iio, Takamasa; Ishiguro, Hiroshi

doi:10.1109/lra.2021.3094779

Cited by 15 publications

(6 citation statements)

References 34 publications

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“…Time horizon [28,34,40,41,45,[47][48][49][50][51]59] 11 (20) Short term [31,33,36,39,[42][43][44]46,54,[56][57][58]60,63,64,[66][67][68][69][70][71][72][73][74][75][76] 26 (46) Medium term [35,38] 2 (4) Long term [10,[22][23][24][25][26][27]29,30,32,37,52,…”

Section: Study Values N (%) Design Dimension and Design Characteristicmentioning

confidence: 99%

“…Intelligence framework [25,27,30,32,36,37,40,46,60,61,65,69,72,74] 14 (25) Rule based [10,22,[54][55][56][57]63,[66][67][68]70,71,73,76] 14 (25) Hybrid [23,24,26,28,29,[33][34][35]38,39,[41][42][43][44][45][47][48][49][50][51][52][53]59,…”

Section: Study Values N (%) Design Dimension and Design Characteristicsmentioning

confidence: 99%

“…Communication modality [22,23,25,26,29,30,[32][33][34][35][36][37][38][39][40][41]43,[45][46][47][48]51,54,55,57,[60][61][62][63][64][65][66]68,69,71,72,74,75] 38 (68) Text only [10,24,27,28,42,44,49,50,52,53,56,59,67,70,…”

Section: Study Values N (%) Design Dimension and Design Characteristicsmentioning

confidence: 99%

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General Characteristics and Design Taxonomy of Chatbots for COVID-19: Systematic Review

Lim¹,

Custodio²,

Sunga³

et al. 2024

J Med Internet Res

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Background A conversational agent powered by artificial intelligence, commonly known as a chatbot, is one of the most recent innovations used to provide information and services during the COVID-19 pandemic. However, the multitude of conversational agents explicitly designed during the COVID-19 pandemic calls for characterization and analysis using rigorous technological frameworks and extensive systematic reviews. Objective This study aims to describe the general characteristics of COVID-19 chatbots and examine their system designs using a modified adapted design taxonomy framework. Methods We conducted a systematic review of the general characteristics and design taxonomy of COVID-19 chatbots, with 56 studies included in the final analysis. This review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select papers published between March 2020 and April 2022 from various databases and search engines. Results Results showed that most studies on COVID-19 chatbot design and development worldwide are implemented in Asia and Europe. Most chatbots are also accessible on websites, internet messaging apps, and Android devices. The COVID-19 chatbots are further classified according to their temporal profiles, appearance, intelligence, interaction, and context for system design trends. From the temporal profile perspective, almost half of the COVID-19 chatbots interact with users for several weeks for >1 time and can remember information from previous user interactions. From the appearance perspective, most COVID-19 chatbots assume the expert role, are task oriented, and have no visual or avatar representation. From the intelligence perspective, almost half of the COVID-19 chatbots are artificially intelligent and can respond to textual inputs and a set of rules. In addition, more than half of these chatbots operate on a structured flow and do not portray any socioemotional behavior. Most chatbots can also process external data and broadcast resources. Regarding their interaction with users, most COVID-19 chatbots are adaptive, can communicate through text, can react to user input, are not gamified, and do not require additional human support. From the context perspective, all COVID-19 chatbots are goal oriented, although most fall under the health care application domain and are designed to provide information to the user. Conclusions The conceptualization, development, implementation, and use of COVID-19 chatbots emerged to mitigate the effects of a global pandemic in societies worldwide. This study summarized the current system design trends of COVID-19 chatbots based on 5 design perspectives, which may help developers conveniently choose a future-proof chatbot archetype that will meet the needs of the public in the face of growing demand for a better pandemic response.

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Section: Study Values N (%) Design Dimension and Design Characteristicmentioning

confidence: 99%

Section: Study Values N (%) Design Dimension and Design Characteristicsmentioning

confidence: 99%

See 1 more Smart Citation

General Characteristics and Design Taxonomy of Chatbots for COVID-19: Systematic Review

Lim¹,

Custodio²,

Sunga³

et al. 2024

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…However, which of these two factors provided the major contribution to enhancing the perception of empathy remains to be verified. Furthermore, this work adopted the experience sharing strategy of previous works [ 15 , 16 , 17 ], which could facilitate the human–robot interaction. What needs to be discussed is whether a robot sharing an experience similar to other people could demonstrate perspective-taking in the human–robot comforting dialogue.…”

Section: Where Nextmentioning

confidence: 99%

“…Moreover, they associated the person-centered message with people’s experience of predicament. In the human–robot interaction field, some studies propose that robots should be allowed to share people-related experiences during interactions [ 15 , 16 , 17 ] to present perspective [ 18 , 19 , 20 ]. They found that the experience sharing strategy can help the robot display mind-attribution, intelligence, while maintaining the long-term interaction to better build the human–robot relationship.…”

Section: Introductionmentioning

confidence: 99%

A Preliminary Study on Realizing Human–Robot Mental Comforting Dialogue via Sharing Experience Emotionally

Deng

Shen

et al. 2022

Sensors

Self Cite

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Mental health issues are receiving more and more attention in society. In this paper, we introduce a preliminary study on human–robot mental comforting conversation, to make an android robot (ERICA) present an understanding of the user’s situation by sharing similar emotional experiences to enhance the perception of empathy. Specifically, we create the emotional speech for ERICA by using CycleGAN-based emotional voice conversion model, in which the pitch and spectrogram of the speech are converted according to the user’s mental state. Then, we design dialogue scenarios for the user to talk about his/her predicament with ERICA. In the dialogue, ERICA shares other people’s similar predicaments and adopts a low-spirit voice to express empathy to the interlocutor’s situation. At the end of the dialogue, ERICA tries to encourage with a positive voice. Subsequently, questionnaire-based evaluation experiments were conducted with the recorded conversation. In the questionnaire, we use the Big Five scale to evaluate ERICA’s personality. In addition, the perception of emotion, empathy, and encouragement in the dialogue are evaluated. The results show that the proposed emotional expression strategy helps the android robot better present low-spirit emotion, empathy, the personality of extroversion, while making the user better feel the encouragement.

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Perception–Intention–Action Cycle in Human–Robot Collaborative Tasks: The Collaborative Lightweight Object Transportation Use-Case

Domínguez-Vidal,

Rodríguez,

Sanfeliu

2024

Int J of Soc Robotics

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This study proposes to improve the reliability, robustness and human-like nature of Human–Robot Collaboration (HRC). For that, the classical Perception–Action cycle is extended to a Perception–Intention–Action (PIA) cycle, which includes an Intention stage at the same level as the Perception one, being in charge of obtaining both the implicit and the explicit intention of the human, opposing to classical approaches based on inferring everything from perception. This complete cycle is exposed theoretically including its use of the concept of Situation Awareness, which is shown as a key element for the correct understanding of the current situation and future action prediction. This enables the assignment of roles to the agents involved in a collaborative task and the building of collaborative plans. To visualize the cycle, a collaborative transportation task is used as a use-case. A force-based model is designed to combine the robot’s perception of its environment with the force exerted by the human and other factors in an illustrative way. Finally, a total of 58 volunteers participate in two rounds of experiments. In these, it is shown that the human agrees to explicitly state their intention without undue extra effort and that the human understands that this helps to minimize robot errors or misunderstandings. It is also shown that a system that correctly combines inference with explicit elicitation of the human’s intention is the best rated by the human on multiple parameters related to effective Human–Robot Interaction (HRI), such as perceived safety or trust in the robot.

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Using an Android Robot to Improve Social Connectedness by Sharing Recent Experiences of Group Members in Human-Robot Conversations

Cited by 15 publications

References 34 publications

General Characteristics and Design Taxonomy of Chatbots for COVID-19: Systematic Review

General Characteristics and Design Taxonomy of Chatbots for COVID-19: Systematic Review

A Preliminary Study on Realizing Human–Robot Mental Comforting Dialogue via Sharing Experience Emotionally

Perception–Intention–Action Cycle in Human–Robot Collaborative Tasks: The Collaborative Lightweight Object Transportation Use-Case

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