Abstract: One of the central societal challenges is to prolong independent living for elderly and promote well. Robotic rehabilitation and assistance is considered one of the enabling technologies with control design playing a significant role. Focussing on sensorimotor rehabilitation and assistance, control should enable to adapt the system to the high inter-personal variability in human motor behavior but also to intra-personal changes over time. Control adaptation is further challenged by the sparsity of person-specific data  because calibration routines need to be brief for user acceptance. Above all, guaranteed safety is one of the key requirements.

In this talk we will present recent results on learning-based control with performance and safety guarantees for highly uncertain systems with particular focus on challenges arising from personalized rehabilitation and assistance. The results will be demonstrated in user intention-driven shared control designs for upper limb rehabilitation and assistance with exoskeletons as well as  functional electrical stimulation. We will introduce the theoretical background and demonstrate and discuss them in the context of clinical practice.

Bio: Sandra Hirche holds the TUM Liesel Beckmann Distinguished Professorship and heads the Chair of Information-oriented Control in the Department of Electrical and Computer Engineering at Technical University of Munich (TUM), Germany (since 2013). She received the diploma engineer degree in Aeronautical and Aerospace Engineering in 2002 from the Technical University Berlin, Germany, and the Doctor of Engineering degree in Electrical and Computer Engineering in 2005 from the Technische Universität München, Munich, Germany. From 2005-2007 she has been a PostDoc Fellow of the Japanese Society for the Promotion of Science at the Fujita Laboratory at Tokyo Institute of Technology, Japan. Prior to her present appointment she has been an Associate Professor at TUM.
Her main research interests include learning, cooperative, and networked control with applications in human-robot interaction, multi-robot systems, and general robotics. She has published more than 200 papers in international journals, books and refereed conferences. She has received multiple awards such as the Rohde & Schwarz Award for her PhD thesis, the IFAC World Congress Best Poster Award in 2005 and – together with students –  Best Paper Awards of IEEE Worldhaptics and IFAC Conference of Manoeuvring and Control of Marine Craft in 2009 and the Outstanding Student Paper Award of the IEEE Conference on Decision and Control 2018. In 2013 she has been awarded with an ERC Starting Grant on the “Control based on Human Models” and in 2019 with the ERC Consolidator Grant on “Safe data-driven control for human-centric systems”. Sandra Hirche is Fellow of the IEEE and received the IEEE Control System Society Distinguished Member Award. 

Abstract: During the past fifteen years there has been remarkable progress in artificial intelligence. More recently, Generative Artificial Intelligence (GenAI) driven disruption marked a significant milestone in cognitive pattern understanding and generation across speech, natural language, image and vision modalities. These improvements have been seamlessly integrated into numerous systems and products, reaching hundreds of millions of users worldwide. Consequently, discussions surrounding the attainment of Artificial General Intelligence (AGI) have transitioned from theoretical speculation to a tangible reality. In this keynote, I will explore the driving forces behind the GenAI revolution and how they are shaping the path toward AGI in the coming decade. Specifically, I will review the challenges and recent progress in the conversational and ambient intelligence field, focusing on how users engage with these systems in increasingly personalized and rich context—such as short- and long-term discourse, spatial awareness, temporal factors, device types, and input/output modalities. Additionally, I will share my perspective on navigating this exciting journey toward a future, where AI systems not only catch up to but potentially exceed human performance in sensory input understanding and cognition.

Bio: Ruhi Sarikaya is a Vice President at Amazon. He is leading the Conversational Assistant Services (namely Alexa AI) organization in Alexa. With his team, he has been building LLM based core AI capabilities around ranking, relevance, natural language understanding, dialog management, contextual understanding, proactive recommendations, personalization, self-learning, metrics and analytics for Alexa. Prior to joining Amazon, he was a principal science manager and the founder of the language understanding and dialog systems group at Microsoft between 2011 and 2016. His group has built the language understanding and dialog management capabilities of Cortana, Xbox One, and the underlying platform. Before Microsoft, he was a research staff member and team lead in the Human Language Technologies Group at the IBM T.J. Watson Research Center for ten years. Prior to IBM, he worked as a researcher at the Center for Spoken Language Research (CSLR) at University of Colorado at Boulder for two years.  He received his BS degree from Bilkent University, MS degree from Clemson University, and Ph.D. degree from Duke University, all in electrical and computer engineering.  He has published over 130 technical papers in refereed journal and conference proceedings and is the inventor of over 90 issued/pending patents. Dr. Sarikaya has served in the IEEE SLTC, the general co-chair of IEEE SLT’12, publicity chair of IEEE ASRU’05, and associate editor of IEEE Trans. on Audio, Speech and Language Processing and IEEE Signal Processing Letters. He was named IEEE SPS Distinguished Industry Speaker and is an IEEE Fellow. He is serving on the advisory board of several engineering departments and universities.