The Speakers
Marynel Vázquez (invited speaker)
Title: Understanding Social Contexts and Scaling Human Supervision: Two Key Challenges for Robot Learning in HRI
Abstract: In this talk, I will describe two key challenges that I believe are important to advance robot learning in Human-Robot Interaction (HRI). The first challenge is about understanding social contexts in HRI. What exactly do we mean by context? How do we model it computationally? The second challenge is about data scarcity in comparison to other related fields, like robot manipulation, computer vision or natural language processing. How can we increase human supervision in HRI? While we don’t have all the answers to these questions yet, my talk will describe promising directions that we believe can help with both challenges. This includes framing context in terms of social situations, organizing context data into graph abstractions, and scaling supervision through interactive online surveys and by leveraging nonverbal human communicative signals — a type of implicit human feedback.
Bio: Marynel Vázquez is an Assistant Professor in Yale’s Computer Science Department, where she leads the Interactive Machines Group. Her research focuses on Human-Robot Interaction (HRI), especially in multi-party and group settings. Marynel is a recipient of the 2022 NSF CAREER Award and two Amazon Research Awards. Her work has been recognized with nominations to paper awards at RO-MAN, IROS and HRI. Prior to Yale, Marynel was a Post-Doctoral Scholar at the Stanford Vision & Learning Lab and obtained her M.S. and Ph.D. in Robotics from Carnegie Mellon University, where she was a collaborator of Disney Research. Before then, she received her bachelor's degree in Computer Engineering from Universidad Simón Bolívar in Caracas, Venezuela.
Henny Admoni (invited speaker)
Title: TBD
Abstract: TBD
Bio: Dr. Henny Admoni is the A. Nico Habermann Assistant Professor in the Robotics Institute at Carnegie Mellon University, where she leads the Human And Robot Partners (HARP) Lab. Dr. Admoni’s research interests include human-robot interaction, assistive robotics, and nonverbal communication. Dr. Admoni holds a PhD in Computer Science from Yale University, and a BA/MA joint degree in Computer Science from Wesleyan University.
Mohamed Chetouani (panelist)
Bio: Prof. Mohamed Chetouani is currently a Full Professor in signal processing and machine learning for human-machine interaction. He is affiliated to the PIRoS (Perception, Interaction et Robotique Sociales) research team at the Institute for Intelligent Systems and Robotics (CNRS UMR 7222), Sorbonne University (formerly Pierre and Marie Curie University). His activities cover social signal processing, social robotics and interactive machine learning with applications in psychiatry, psychology, social neuroscience and education. He was the coordinator of the ANIMATAS H2020 Marie Sklodowska Curie European Training Network (2018-2022). Since 2019, he is the President of the Sorbonne University Ethics Committee. He was involved in several educational activities including organization of summer schools. He is member of the EU Network of Human-Centered AI. He is General Chair of ACM ICMI 2023. He is in charge of the Inclusion of Students with Disabilities for the Faculty of Science and Engineering of Sorbonne University.
Jens Kober (panelist)
Bio: Jens Kober is an associate professor at the TU Delft, Netherlands. He worked as a postdoctoral scholar jointly at the CoR-Lab, Bielefeld University, Germany and at the Honda Research Institute Europe, Germany. He graduated in 2012 with a PhD Degree in Engineering from TU Darmstadt and the MPI for Intelligent Systems. For his research he received the annually awarded Georges Giralt PhD Award for the best PhD thesis in robotics in Europe, the 2018 IEEE RAS Early Academic Career Award, the 2022 RSS Early Career Award, and has received an ERC Starting grant. His research interests include motor skill learning, (deep) reinforcement learning, imitation learning, interactive learning, and machine learning for control.
Stefanie Tellex (panelist)
Bio: Stefanie Tellex is an Associate Professor of Computer Science at Brown University. She aims to empower every person with a collaborative robot partner. Her research seeks to construct robots that seamlessly use natural language to communicate with humans. In twenty years, every home will have a personal robot that can perform tasks such as clearing the dinner table, doing laundry, and preparing dinner. As these machines become more powerful and more autonomous, it is critical to develop methods for enabling people to tell them what to do. Robots that can communicate with people using language can respond appropriately to commands given by humans, ask questions when they are confused, and request help when they get stuck. She applies probabilistic methods, corpus-based training, and decision theory to develop interactive robotic systems that can understand and generate natural language. I completed my Ph.D. at the MIT Media Lab in 2010, where she developed models for the meanings of spatial prepositions and motion verbs. Her postdoctoral work at MIT CSAIL focused on creating robots that understand natural language. She has published at SIGIR, HRI, RSS, AAAI, IROS, and ICMI, winning Best Student Paper at SIGIR and ICMI. She was named one of IEEE Spectrum’s AI’s 10 to Watch and won the Richard B. Salomon Faculty Research Award at Brown University.
Dylan Glas (panelist)
Bio: Dylan Glas is a senior applied scientist at Amazon Lab126, where his team conducts research and development for HRI features for the Amazon Astro robot. He was a senior researcher and software architect for both the ERICA conversational android at Hiroshi Ishiguro Laboratories and the Xiaoyi Wizard educational robot at Huawei. He has also worked in the Tangible Media Group at the MIT Media Lab and the Intelligent Robotics and Communication Laboratory at ATR in Japan. He received his PhD from the Intelligent Robotics Laboratory at Osaka University, where he later served as a Guest Associate Professor. His current research focuses on understanding the social use of space and developing interactive navigational behavior for social robots in home environments.