A Modular Structured Architecture Using Smart Devices for Socially-Embedded Robot Partners

Recently, robot architectures with various structures have been developed to improve the human quality of life. Such a robot needs various capabilities such as learning, inference, and prediction for human interaction, and such capabilities are interconnected with each other as a whole system. In the development of a socially-embedded robot partner, human-robot interaction plays an important role. Therefore, in order to develop a socially embedded robot partner, we must consider human communication system. Human Cognition, Emotion, and Behavior should be considered in the development process of the robot partner, and if these factors are fully reflected in the robot partner, then the robot can be used as a socially-friendly robot partner. This book chapter is organized as follows: First, we describe the hardware and software structures. Next, we discuss the cognitive model of the robot partners. Third, we discuss interaction content design for various services. Finally, we discuss the contents of society implementation, and discuss the applicability of robots for social utilization. A Modular Structured Architecture Using Smart Devices for SociallyEmbedded Robot Partners

[1]  Bruce A. MacDonald,et al.  Acceptance of Healthcare Robots for the Older Population: Review and Future Directions , 2009, Int. J. Soc. Robotics.

[2]  Jean Piaget,et al.  Part I: Cognitive development in children: Piaget development and learning , 1964 .

[3]  I. René J. A. te Boekhorst,et al.  Learning about natural human-robot interaction styles , 2006, Robotics Auton. Syst..

[4]  N. Kubota,et al.  Robot Partner System for elderly people care by using sensor network , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[5]  Peter Ford Dominey,et al.  Robot Cognitive Control with a Neurophysiologically Inspired Reinforcement Learning Model , 2011, Front. Neurorobot..

[6]  Séverin Lemaignan,et al.  Artificial cognition for social human-robot interaction: An implementation , 2017, Artif. Intell..

[7]  M.K. Habib,et al.  Human adaptive and friendly mechatronics (HAFM) , 2008, 2008 IEEE International Conference on Mechatronics and Automation.

[8]  Thomas B. Sheridan,et al.  Human–Robot Interaction , 2016, Hum. Factors.

[9]  Naoyuki Kubota,et al.  Communication based on Frankl's psychology for humanoid robot partners using emotional model , 2013, 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[10]  M. Lee,et al.  Determining informative priors for cognitive models , 2018, Psychonomic bulletin & review.

[11]  Naoyuki Kubota,et al.  Interaction content design for information support based on robot partner , 2017, 2017 10th International Conference on Human System Interactions (HSI).

[12]  Naoyuki Kubota,et al.  Facial and gestural expression generation for robot partners , 2014, 2014 International Symposium on Micro-NanoMechatronics and Human Science (MHS).

[13]  Jinseok Woo,et al.  Emotional Empathy Model For Robot Partners Using Recurrent Spiking Neural Network Model With Hebbian-Lms Learning , 2017 .

[14]  C. Breazeal,et al.  Robots that imitate humans , 2002, Trends in Cognitive Sciences.

[15]  Naoyuki Kubota,et al.  A Socially Interactive Robot Partner Using Content-Based Conversation System for Information Support , 2018, J. Adv. Comput. Intell. Intell. Informatics.

[16]  Mark Coeckelbergh,et al.  Humans, Animals, and Robots: A Phenomenological Approach to Human-Robot Relations , 2011, Int. J. Soc. Robotics.

[17]  R. Dolan,et al.  Emotion, Cognition, and Behavior , 2002, Science.

[18]  Naoyuki Kubota,et al.  Integrated Robotic Control System for Public Nursing , 2018, 2018 World Automation Congress (WAC).

[19]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[20]  Naoyuki Kubota,et al.  Weather forecast support system implemented into robot partner for supporting elderly people using fuzzy logic , 2017, 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS).

[21]  Naoyuki Kubota,et al.  Verbal conversation system for a socially embedded robot partner using emotional model , 2015, 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).

[22]  Tamim Asfour,et al.  A cognitive architecture for a humanoid robot: a first approach , 2005, 5th IEEE-RAS International Conference on Humanoid Robots, 2005..

[23]  A. Dean,et al.  Effects of social support from various sources on depression in elderly persons. , 1990, Journal of health and social behavior.

[24]  Pat Langley,et al.  Cognitive architectures: Research issues and challenges , 2009, Cognitive Systems Research.

[25]  Janos Botzheim,et al.  A modular cognitive model of socially embedded robot partners for information support , 2017, ROBOMECH Journal.

[26]  Naoyuki Kubota,et al.  A novel multimodal communication framework using robot partner for aging population , 2015, Expert Syst. Appl..

[27]  P. Dario,et al.  Supporting active and healthy aging with advanced robotics integrated in smart environment , 2016 .

[28]  Richard M. Young,et al.  The Role of Cognitive Architecture in Modeling the User: Soar's Learning Mechanism , 1997, Hum. Comput. Interact..

[29]  Naoyuki Kubota,et al.  Human Posture Recognition for Estimation of Human Body Condition , 2019, J. Adv. Comput. Intell. Intell. Informatics.

[30]  Miranda Kit-Yi Wong,et al.  Using a social robot to teach gestural recognition and production in children with autism spectrum disorders , 2018, Disability and rehabilitation. Assistive technology.

[31]  Naoyuki Kubota,et al.  Nonverbal Communication Based on Instructed Learning for Socially Embedded Robot Partners , 2019, J. Adv. Comput. Intell. Intell. Informatics.

[32]  Janos Botzheim,et al.  System Integration for Cognitive Model of a Robot Partner , 2017 .

[33]  Scott D. Brown,et al.  Different Ways of Linking Behavioral and Neural Data via Computational Cognitive Models. , 2016, Biological psychiatry. Cognitive neuroscience and neuroimaging.