Speakers
Dr. Sylvain Calinon
Dr. Sylvain Calinon is a Senior Research Scientist at the Idiap Research Institute and a Lecturer at the Ecole Polytechnique Fédérale de Lausanne (EPFL). He heads the Robot Learning & Interaction group at Idiap, with expertise in human-robot collaboration, robot learning from demonstration and model-based optimization. The approaches developed in his group can be applied to a wide range of applications requiring manipulation skills, with robots that are either close to us (assistive and industrial robots), parts of us (prosthetics and exoskeletons), or far away from us (shared control and teleoperation).
Prof. Georgia Chalvatzaki
As of April 2023, Georgia is a Full Professor for Interactive Robot Perception & Learning at the Computer Science Department of the Technical University of Darmstadt and Hessian.AI. Before that, she was an Assistant Professor since February 2022, and Independent Research Group Leader from March 2021, after getting the renowned Emmy Noether Programme (ENP) fund of the German Research Foundation (DFG). This project was awarded within the ENP Artificial Intelligence call of the DFG – only 9 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 six years. In her research group, PEARL (previously iROSA), Dr. Chalvatzaki and her team propose new methods at the intersection of machine learning and classical robotics, taking the research for embodied AI robotic assistants one step further. The research in PEARL proposes novel methods for combined planning and learning to enable mobile manipulator robots to solve complex tasks in house-like environments, with the human-in-the-loop of the interaction process.
Dr. SiQi Zhou
SiQi Zhou obtained her Ph.D. from the University of Toronto in 2022 and currently is a Senior Scientist in the Learning Systems and Robotics Lab led by Prof. Angela Schoellig at the Technical University of Munich. Her research is centred on the development of mathematical frameworks and algorithms for safe robot decision-making. The overarching goal is to enable robots to operate safely within unstructured environments and intelligently accomplish tasks alongside humans. Her research interests include control theory, machine learning, and robot decision-making with a focus on semantically safe behaviours
Lukas Brunke
Lukas Brunke is a PhD candidate at the University of Toronto under the supervision of Professor Angela Schoellig. Currently, he is a researcher at the Learning Systems and Robotics Lab at the Technical University of Munich. His research combines model-based control algorithms with machine learning methods for safe, high-performance robotic applications in uncertain and dynamic environments.