Invited Speakers

Prof. Emilia Barakova

Eindhoven University of Technology

Emilia Barakova received her Ph.D. in Mathematics and Natural Sciences from the University of Groningen in 1999, and her master’s degree in Electronics and Automation engineering from the Technical University of Sofia in Bulgaria. She is presently affiliated with the Industrial Design department and serves as the Head of the Social Robotics Lab at the Eindhoven University of Technology. She formerly worked at Riken Brain Science Institute, Wako-shi, Japan, the German-Japanese Robotics Research Lab, Kitakyushu, Japan, the University of Groningen in the Netherlands, and the Bulgarian Academy of Sciences. Barakova specializes in embodied social interaction with and through technology and social and cognitive robotics.  She has expertise in modeling social behavior by merging artificial intelligence, cognitive sciences, and robotics. Her present research focuses on the use of social robots for education and special education (i.e. social skills training of children with autism spectrum disorders),  and enhancing the well-being of people with dementia and intellectual disabilities. Barakova has served as the program chair for several conferences (including IJSR, IEEE RO-MAN, and IEEE Hybrid Intelligent Systems), and she is an Associate Editor of the International Journal of Social Robotics, as well as an editor of Personal and Ubiquitous Computing, Interaction Studies, and Transactions of Human-Machine Systems. She has co-authored over 200 peer-reviewed papers.



Details of the Talk

TITLE: Combining Social Robots and Wearables to Promote Positive Affect and Engagement in Assistive Tasks

ABSTRACT: 

One of the key factors in the success of assistive robots is their ability to engage and connect with people, provide emotional and social support, encourage positive behavior, and improve motivation and engagement. Various user groups such as children with ASD, elderly with dementia, people with intellectual disabilities, and young children in postoperative care struggle to adequately self-report and explain their degrees of discomfort, pain, and worry. To address this, we used interaction design methods and a combination of wearables, robots, and mobile apps to transform social robots into effective tools for promoting pleasant affect, engagement, and distraction from pain and loneliness in assistive tasks. Furthermore, we incorporated contextual aspects (e.g., hospital or care home), the patient/client journey, and personal needs, as well as the involvement of caregivers and parents, into our robot therapies.


Prof. Ginevra Castellano 

Uppsala University

Ginevra Castellano Ginevra Castellano is a Professor in Intelligent Interactive Systems at the Department of Information Technology of Uppsala University, Sweden, where she leads the Uppsala Social Robotics Lab. Her research is in the area of social robotics and human-robot interaction, addressing questions on how we can build human-robot interactions that are trustworthy, including human-robot relationship formation, robot ethics, robot autonomy and human oversight, gender fairness, robot transparency  and trust, both from the perspective of developing computational skills for robotic systems, and  their evaluation with human users to study acceptance and social consequences. She has published over 100 papers on these topics, receiving around 5000 citations. From 2012 to 2016 she was the coordinator of the EMOTE (EMbOdied perceptive Tutors for Empathy-based learning) project, which developed educational robots to support teachers in a  classroom environment.

Castellano was a General Chair at IVA 2017 and HRI 2023.

She was the recipient of the 10-Year Technical Impact Award at the ACM International Conference on  Multimodal Interaction 2019 and the Frontiers in Robotics and AI 2021 Outstanding Associate Editor  Award.

She is an Associate Editor of Frontiers in Robotics and AI and the ACM Transactions on  Human-Robot Interaction.


Details of the Talk

TITLE: Social-robots for perinatal depression screening: users and experts' views and ethical considerations

ABSTRACT:

Perinatal depression (PND) affects as many as 10% of women during pregnancy or after childbirth. It is a serious and potentially life-threatening disorder with high societal costs. Research shows that psychosocial interventions may decrease depressive symptoms for women affected by PND. However, in order to receive treatment, a clinical diagnosis of depression is required. This currently entails a structured clinical interview with a skilled physician. However, access to skilled personnel with training to perform the clinical interviews in primary care can vary substantially, which can lead to long waiting times or an unstructured interview with lower diagnostic accuracy. According to a recent review, up to 69% of PND cases go undetected and only 6% receive adequate treatment.

At the same time, socially assistive robots (SARs) have shown potential in mental healthcare.

In this talk I will present my group's research on how SARs may be used to assist clinicians in screening and diagnosis of PND. Through a set of interview studies with users and experts, I will discuss envisioned requirements for SARs in PND screening, as well as ethical considerations on their roles, capabilities and appearance.

Hae Won Park 

Amazon Lab126 & MIT Media Lab, USA

Hae Won Park, PhD, is a Research Scientist & PI at MIT Media Lab where she leads provocative research in long-term personalization and relationship building in HRI. Now as an Amazon Visiting Scholar, she is teaming up with Lab126 to develop consumer home robots companions. Her research principles are deeply grounded in real-world problems impacting domains such as education, aging, and healthcare.





Details of the Talk

TITLE: Personalized Interaction Policies - Engaging our cognitive and affective states with social robot partners and building relationships

ABSTRACT: 

In this talk, I will highlight a number of provocative research findings from our recent long-term deployment of social agents in homes, schools, and living communities engaging families, young children, and older adults. We employ an affective reinforcement learning approach to personalize the agent’s actions to modulate users' engagement and maximize the interaction benefit. Our results show that the interaction with an AI companion influences users’ beliefs, learning, and how they interact with others. The affective personalization boosts these effects and helps sustain long-term engagement. During our deployment studies, we observed that people treat and interact with artificial agents as social partners and catalysts. We also learned that the effect of the interaction strongly correlates to the social relational bonding users built with the agent. Now, when designing longterm social AI partners, how should such relational dimensions come into play?