Georgia Chalvatzaki is an Assistant Professor and the research leader of the intelligent robotic systems for assistance (iROSA) group at TU Darmstadt. She has been accepted into the renowned Emmy Noether Programme (ENP) of the German Research Foundation (DFG) in 2021. In her research group iROSA, her team will research the topic of "Robot Learning of Mobile Manipulation for Assistive Robotics", investigating novel methods for combined planning and learning for enabling mobile manipulator robots to solve complex tasks in house-like environments, with the human-in-the-loop of the interaction process. She is a co-chair of the IEEE RAS technical committee of Mobile Manipulation, co-chair of the IEEE RAS Women in Engineering committee, and she has been voted the “AI-Newcomer” for 2021 by the German Information Society.
Before that, she was a Postdoctoral researcher from October 2019 till February 2021 at the Intelligent Autonomous Systems (IAS) group of Prof. Jan Peters in the Department of Computer Science at TU Darmstadt. She completed her Ph.D. studies in 2019 at the Intelligent Robotics and Automation Lab, advised by Prof. Costas Tzafestas and Prof. Petros Maragos, of the National Technical University of Athens, in Greece. Her thesis topic concerns the topic of "Human-Centered Modeling for Assistive Robotics: Stochastic Estimation and Robot Learning in Decision-Making."
Talk Title: Robot Learning for Intelligent Assistants
Societal facts like the increase in the elderly population, the lack of nursing staff, the hectic rhythms of everyday life, and the recent unprecedented situation of the Covid-19 pandemic, make intelligent robotic assistants more urgent than ever. The embodied AI robotic assistants are at the epicenter of modern robotics and AI research, spanning their applications from domestic environments to hospitals, workhouses to agricultural development, etc. In this talk, I will focus on sub-problems of the field that I have addressed over the last years, namely, covering the topics of human activity recognition and understanding; the combination of classical and machine learning methods for robot action planning and control; reinforcement learning methods for adaptive Human-Robot Interaction (HRI); and methods for learning to plan long-horizon tasks. Finally, I will present the open research problems that my group iROSA and I will address in the following years to enable mobile manipulation robots to operate in dynamic, unstructured environments while focusing on safety in HRIs.