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Georgia Chalvatzaki, a postdoctoral researcher at the Intelligent Autonomous Systems group (IAS) in the Department of Computer Science at TU Darmstadt, has been accepted into the renowned Emmy Noether Programme (ENP) of the German Research Foundation (DFG).
This project was awarded within the ENP Artificial Intelligence call of the DFG – only nine proposals out of 91 proposals were selected for funding. It enables outstanding young scientists to qualify for a university professorship by independently leading a junior research group over a period of six years. The funding of about one million Euros for the first three years comprises two doctoral researchers and a TIAGo++ service robot.
In her research group iROSA, the robotics expert and her new team will conduct research on the topic of "Robot Learning for Mobile Manipulation in Assistive Robotics". Chalvatzaki proposes new methods at the intersection of Machine Learning and Classical Robotics, taking one step further the research for embodied AI robotic assistants. As planning for assistive tasks requires impractical computational time, coupling planning with learning methods is key to advancing the state-of-the-art in the field of mobile manipulation. Before the introduction of deep Reinforcement Learning, learning methods were not able to scale well to high-dimensional problems, thus prohibiting their use in real-world problems. The research in iROSA proposes 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.
Chalvatzaki completed her Ph.D. studies in 2019 at the Intelligent Robotics and Automation Lab at the Electrical and Computer Engineering School of the National Technical University of Athens, Greece, with her thesis "Human-Centered Modeling for Assistive Robotics: Stochastic Estimation and Robot Learning in Decision Making."
Her research at the Computer Science department of TU Darmstadt has been about human-robot collaboration and joint action. In her recent work, she focused on robotic grasping, manipulation, and motion prediction, introducing novel methods for orientation attentive grasp synthesis, accelerated skill learning, and human intention prediction.
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