Karen Quigley

Department of Psychology, Northeastern University

Dr. Quigley's basic science work examines the psychophysiological correlates of affective experience including emotions and stress, the role of interoception in affective experience, and how the body and brain work together to construct our affective experience. She is an experimental psychologist and psychophysiologist with more than 25 years' experience conducting research with a wide range of measures and samples, including people who have experienced negative functional impacts after major life events. She is a former president of the Society for Psychophysiological Research, and a Fellow of both the Association for Psychological Science and the Academy of Behavioral Medicine Research. She was formerly an Associate Editor for Psychophysiology, where she is currently a Consulting Editor. She is also on the editorial board for the new journal, Affective Science. In early work, she co-authored a model for quantifying and assessing autonomic control of cardiovascular responses during stressors in animals and humans, including in early life, and validated noninvasive indices of autonomic control of the heart for use in children and adults. New work focuses on better understanding the wide variation across people and contexts in how physiological features can map to affective experiences. To enable this work, she developed a new physiologically-triggered experience sampling method. Other work focuses on the role of biological features (such as energy regulation) and contextual features (such as exposure to major stressful life events) in shaping affective experience. In her applied work, she uses health technology as a means to intervene and enable positive lifestyle change, with the goal of improving health outcomes such as sleep, physical activity, and pain.

Title:
Understanding Variation in Affective Experience using Physiology

Maja Mataric

Chan Soon-Shiong Distinguished Professor of Computer Science, Neuroscience, and Pediatrics, University of Southern California

Maja Mataric is Chan Soon-Shiong Professor of Computer Science, Neuroscience, and Pediatrics at USC, founding director of the Robotics and Autonomous Systems Center and Vice Dean for Research. Her PhD and MS are from MIT, and BS from Kansas University. She is Fellow of AAAS, IEEE, and AAAI, recipient of the Presidential Award for Excellence in Science, Mathematics & Engineering Mentoring, Anita Borg Institute Women of Vision for Innovation, NSF Career, MIT TR35 Innovation, and IEEE RAS Early Career Awards, is highly active in K-12 outreach (leading the USC Engineering K-12 STEM Center) and in mentoring of women and under-represented groups in engineering, and authored “The Robotics Primer” (MIT Press). A pioneer of the filed of socially assistive robotics, her research team is developing human-robot interaction methods for convalescence, rehabilitation, training, and education for children with autism spectrum disorders, stroke and traumatic brain injury survivors, and individuals with Alzheimer's Disease. She is also co-founder of Embodied, Inc.

Title:
Socially Assistive Robotics Right Now: The Need for Personalized Embodied Systems for In-Home Support of Health, Wellness, Education, and Training

Abstract:
The nexus of advances in robotics, NLU, and machine learning has created opportunities for personalized robots for the ultimate robotics frontier, the home. The current pandemic has both caused and exposed unprecedented levels of health & wellness, education, and training needs worldwide, which must increasingly be addressed in the home. Socially assistive robotics has the potential to address those needs through personalized and affordable in-home support. This talk will discuss human-robot interaction methods for socially assistive robotics that utilize multi-modal interaction data and expressive and persuasive robot behavior to monitor, coach, and motivate users to engage in health, wellness, education and training activities. Methods and results will be presented that include modeling, learning, and personalizing user motivation, engagement, and coaching of healthy children and adults, stroke patients, Alzheimer's patients, and children with autism spectrum disorders, in short and long-term (month+) deployments in schools, therapy centers, and homes. Research and commercial implications and pathways will be discussed.

Hideki Kozima

Graduate School of Education, Tohoku University, Japan

Hideki Kozima is a professor at the Graduate School of Education, Tohoku University, since 2018. He received his Ph.D. in Computer Science and Information Mathematics from the University of Electro-Communications (Tokyo, Japan) in 1994. He worked as a research scientist at the National Institute of Information and Communications Technology (NICT; headquarter in Tokyo) from 1994 to 2008. In 2001, he and Dr. Jordan Zlatev (Lund University, Sweden) co-founded a research community under the title of “Epigenetic Robotics” and started a series of international conferences, which is known as “IEEE ICDL-EpiRob” today. In 2008, he moved to Miyagi University (Miyagi, Japan), serving as a professor and vice president. His current research interest includes cognitive developmental robotics, autism and developmental disorders, and information technologies for education and therapy.

Title:
Epigenesis of Social Intelligence: Twenty-year Research Since EpiRob 2001

Abstract:
ICDL-EpiRob has its roots in ICDL and EpiRob. For the latter, EpiRob, Jordan Zlatev (Lund University, Sweden) and myself (CRL/NICT, Japan) started in 2001 as a series of conferences on “Epigenetic Robotics”, which aimed at “modeling cognitive development in robotic systems”. For the last 20 years, EpiRob contributed to establishing a new research field and research community for modeling “embodiment and situatedness”, “development and learning”, “communication and socialization”, “language and semiosis” and so on.

In that large context, we pursued an engineering model of “social development”, especially “human communication in the preverbal stage”, and “autism” as the other side of a coin. I developed a child-like humanoid, “Infanoid”, which is capable of making eye-contact and joint attention with human caregivers. Joint attention is one of the most important keys to communicative development through social interaction with caregivers. Infanoid had been used in psychological experiments to investigate how humans, especially children, interpret its gaze and behavior. We found that even 4-year-olds read the “mind” of the robot. Then, we move forward to making a simpler robot for psychological experiments with younger children. The new robot, “Keepon”, has a simple appearance, but it is still capable of pre-verbal communication as that of Infanoid. We found that children of different ages, from 6 months old, understand and acted on Keepon differently according to their developmental stages.

We intensively used those robots, especially Keepon, for autism research. Autism is characterized by (1) deficits in communication in both verbal and non-verbal ways and (2) restricted imagination, such as repetitive and/or restricted behavior and interest. Understanding autism and understanding human communication are the two sides of a coin. Keepon engaged in a longitudinal observation at a daycare center for autistic children for over 10 years, where we learned that Keepon's simple appearance worked well in establishing meaningful interaction with the children. Based on the observation, we hypothesized that autistic children have difficulty in transforming high-dimensional perceptual information (for instance, visual/auditory image of others) into low-dimensional social meanings (for instance, attention/emotion of others). The actual mechanism of this information transformation is still unknown.

To understand the social transformer, which we called “mentalizing filter”, we are currently looking into brains. Based on the recent findings of the higher density in mini-columnar structure in autistic brains, we assumed the following points. (1) The density of the cortical structure determines “cognitive granularity”, especially in perceptual and linguistic categories. (2) The cognitive granularity is related to the abstraction level in mentalizing others' behavior, at which we explain others' behavior in terms of intentions or states of mind. And, (3) such finer cognitive granularity will produce the diversity of ASD's behavioral symptoms, including social communication disorders and the restricted interest and behavior. We are currently working on theorizing and experimenting (in medical and computational ways) this grand hypothesis.

Kozima, H., & Yano, H. (2001). A Robot That Learns to Communicate with Human Caregivers. International Workshop on Epigenetic Robotics (EpiRob), 1st.

Gianluca Baldassarre

Istituto di Scienze e Tecnologie della Cognizione, Consiglio Nazionale delle Ricerche (ISTC-CNR), Italy

Gianluca Baldassarre received the B.A. and M.A. degrees in economics and the M.Sc. degree in cognitive psychology and neural networks from the University of Rome “La Sapienza,” Rome, Italy, in 1998 and 1999, respectively, and the Ph.D. degree in computer science with the University of Essex, Colchester, U.K., in 2003, with a focus on planning with neural networks. He was a Post-Doctoral Fellow with the Italian Institute of Cognitive Sciences and Technologies, National Research Council, Rome, researching on swarm robotics, where he has been a Researcher, since 2006, and coordinates the Research Group that he founded called the Laboratory of Computational Embodied Neuroscience. From 2006 to 2009, he was a Team Leader of the EU Project “ICEA—Integrating Cognition Emotion and Autonomy” and the Coordinator of the European Integrated Project “IM-CLeVeR— Intrinsically-Motivated Cumulative-Learning Versatile Robots,” from 2009 to 2013, and is currently Team Leader of the EU Project “GOAL-Robots – Goal-based Open-ended Autonomous Learning Robots”. He has over 100 international peer-review publications. His cur- rent research interests include cumulative learning of multiple sensorimotor skills driven by extrinsic and intrinsic motivations. He studies these topics with two interdisciplinary approaches: with computational models constrained by data on brain and behavior, aiming to understand the latter ones and with machine-learning/robotic approaches, aiming to produce technologically useful robots.

Title:
What Are Intrinsic Motivations? A Biological and Robotics Perspective

Abstract:
Intrinsic motivations (IMs) have for long been studied by psychologists, and lately by cognitive science and computer modelling that is greatly contributing to give operational definitions and taxonomies of them. The identification of IMs is challenging as they involve several functions and mechanisms that interplay in complex ways within brain and robot architectures. Following the talk I gave at ICDL2011, I contribute here to disentangling these aspects from a biology perspective and add also a second perspective from the robotics perspective. First, by contrasting them to extrinsic motivations (EMs), I give a general definition of IMs as fundamental drives that can guide the autonomous acquisition of knowledge and skills. Then I focus on what I call epistemic IMs, and proceed to distinguish between novelty-based, prediction-based and competence-based IMs. I then present few examples of how EMs and such IMs can be implemented in the brain. I then proceed to consider IMs from the perspective of robots. To this purpose, I introduce the EU funded project GOAL-Robots, focussed on IMs for the acquisition of skills, that is stressing how IMs are very important to lead to the autonomous acquisition of goals that in turn support the acquisition of skills. I then illustrate this more in detail with two examples of robotics models able to fully autonomously acquire goals and skills. Overall, the presentation highlights the importance that IMs have as drives for the non-materialistic development of humans and for supporting open-ended learning in robots.

Baldassarre, G. (2011). What Are Intrinsic Motivations? A Biological Perspective. IEEE International Conference on Development and Learning (ICDL), 2, 1–8.