Focus area: Foundations of ML: Robot Learning

Nick Heppert is a Doctoral Researcher at the University of Freiburg, part of ELLIS and an ELIZA Fellow since March 2024. His research focuses on robot learning, with an emphasis on perception for robotic manipulation. He holds an M.Sc. in Autonomous Systems from TU Darmstadt and has conducted research at Stanford University and the Toyota Research Institute. His most influential publication, “CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects” (CVPR 2023), addresses generalizable 3D perception of articulated objects for robotics. He has published at top robotics and machine learning conferences, including IROS, ICRA, CVPR, and CoRL. Nick actively contributes to the research community as a reviewer for RA-L, ICRA, and IROS, and through organizing academic workshops. He has also mentored over 10 Master’s students at the University of Freiburg. His work aims to bridge perception and manipulation to enable more capable and adaptable robotic systems. He is also part of the interdisciplinary ReScaLe project, which focuses on developing responsible and scalable learning for robots assisting humans, with particular attention to social impact and regulatory dimensions.

I am an ELLIS PhD student supervised by Dieter Büchler (University of Alberta, Max Planck Institute for Intelligent Systems), Ingmar Posner (University of Oxford), and Bernhard Schölkopf (Max Planck Institute for Intelligent Systems). My research interests generally lie in reinforcement learning and robotics. More concretely, I am interested in the role of action representations in reinforcement learning, discovering and exploiting structure in the learning process, and applying reinforcement learning to muscular robots for solving dynamic tasks.

I am working on enabling autonomous systems to efficiently reason on their environment and actions by combining the powers of probability theory with formal logic. To achieve continual reasoning under real-time constraints, exploiting properties of the agent’s perception and knowledge of its domain can help us to overcome the costs of probabilistic inference. Hence, my research focuses on creating reactive systems that are built on a foundation of probabilistic logic and message passing between individual models.

Jan Peters is a full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department of the Technische Universitaet Darmstadt since 2011, and, at the same time, he is the dept head of the research department on Systems AI for Robot Learning (SAIROL) at the German Research Center for Artificial Intelligence (Deutsches Forschungszentrum für Künstliche Intelligenz, DFKI) since 2022. He is also is a founding research faculty member of the Hessian Center for Artificial Intelligence. Jan Peters has received the Dick Volz Best 2007 US PhD Thesis Runner-Up Award, the Robotics: Science & Systems – Early Career Spotlight, the

INNS Young Investigator Award, and the IEEE Robotics & Automation Society’s Early Career Award as well as numerous best paper awards. In 2015, he received an ERC Starting Grant and in 2019, he was appointed IEEE Fellow, in 2020 ELLIS fellow and in 2021 AAIA fellow.

Despite being a faculty member at TU Darmstadt only since 2011, Jan Peters has already nurtured a series of outstanding young researchers into successful careers. These include new faculty members at leading universities in the USA, Japan, Germany, Finland and Holland, postdoctoral scholars at top computer science departments (including MIT, CMU, and Berkeley) and young leaders at top AI companies (including Amazon, Boston Dynamics, Google and Facebook/Meta).

Jan Peters has studied Computer Science, Electrical, Mechanical and Control Engineering at TU Munich and FernUni Hagen in Germany, at the National University of Singapore (NUS) and the University of Southern California (USC). He has received four Master’s degrees in these disciplines as well as a Computer Science PhD from USC. Jan Peters has performed research in Germany at DLR, TU Munich and the Max Planck Institute for Biological Cybernetics (in addition to the institutions above), in Japan at the Advanced Telecommunication Research Center (ATR), at USC and at both NUS and Siemens Advanced Engineering in Singapore. He has led research groups on Machine Learning for Robotics at the Max Planck Institutes for Biological Cybernetics (2007-2010) and Intelligent Systems (2010-2021).