Focus area: Applications in Autonomous Systems

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.

Abhinav Valada is a Full Professor (W3) at the University of Freiburg, where he directs the Robot Learning Lab. He is a member of the Department of Computer Science, the BrainLinks-BrainTools center, and a founding faculty of the ELLIS unit Freiburg. Abhinav is a DFG Emmy Noether AI Fellow, Scholar of the ELLIS Society, IEEE Senior Member, and Chair of the IEEE Robotics and Automation Society Technical Committee on Robot Learning.

He received his PhD (summa cum laude) working with Prof. Wolfram Burgard at the University of Freiburg in 2019, his MS in Robotics from Carnegie Mellon University in 2013, and his BTech. in Electronics and Instrumentation Engineering from VIT University in 2010. After his PhD, he worked as a Postdoctoral researcher and subsequently an Assistant Professor (W1) from 2020 to 2023. He co-founded and served as the Director of Operations of Platypus LLC from 2013 to 2015, a company developing autonomous robotic boats in Pittsburgh, and has previously worked at the National Robotics Engineering Center and the Field Robotics Center of Carnegie Mellon University from 2011 to 2014.

Abhinav’s research lies at the intersection of robotics, machine learning, and computer vision with a focus on tackling fundamental robot perception, state estimation, and planning problems to enable robots to operate reliably in complex and diverse domains. The overall goal of his research is to develop scalable lifelong robot learning systems that continuously learn multiple tasks from what they perceive and experience by interacting with the real world. For his research, he received the IEEE RAS Early Career Award in Robotics and Automation, IROS Toshio Fukuda Young Professional Award, NVIDIA Research Award, AutoSens Most Novel Research Award, among others. Many aspects of his research have been prominently featured in wider media such as the Discovery Channel, NBC News, Business Times, and The Economic Times.

Michael Black is a founding director at the Max Planck Institute for Intelligent Systems, an Honorarprofessor at the University of Tübingen, and is the Speaker of Cyber Valley. Black received his B.Sc. from the University of British Columbia (1985), M.S. from Stanford (1989), and Ph.D. from Yale University (1992). He has held positions at the University of Toronto, Xerox PARC, Brown University, and Amazon. He is a recipient of the PAMI Distinguished Researcher Award, the 2022 and 2010 Koenderink Prize, the 2013 Helmholtz Prize, the 2020 Longuet-Higgins Prize, and the 2024 SIGGRAPH Asia Test-of-Time award. He is a member of two national academies (Germany and Sweden). He has co-founded two companies: Body Labs and Meshcapade.

Gerard Pons-Moll is a Professor at the University of Tübingen endowed by the Carl Zeiss Foundation, at the department of Computer Science. He is also core faculty at the Tübingen AI Center and head of the Emmy Noether independent research group “Real Virtual Humans”, senior researcher at the Max Planck for Informatics (MPII) in Saarbrücken, Germany, and faculty at the IMPRS-IS (International Max Planck Research School – Intelligent Systems in Tübingen) and faculty at Saarland Informatics Campus. His research lies at the intersection of computer vision, computer graphics and machine learning — with special focus on analyzing people in videos, and creating virtual human models by “looking” at real ones. His research has produced some of the most advanced statistical human body models of pose, shape, soft-tissue and clothing (which are currently used for a number of applications in industry and research), as well as algorithms to track and reconstruct 3D people models from images, video, depth, and IMUs.

His work has received several awards including the prestigious Emmy Noether Grant (2018), a Google Faculty Research Award (2019), a Facebook Reality Labs Faculty Award (2018), and recently the German Pattern Recognition Award (2019), which is given annually by the German Pattern Recognition Society to one outstanding researcher in the fields of Computer Vision and Machine Learning. In 2020 he received a Snap-Research gift. His work got Best Papers Awards BMVC’13, Eurographics’17, 3DV’18 and CVPR’20 and has been published at the top venues and journals including CVPR, ICCV, Siggraph, Eurographics, 3DV, IJCV and PAMI. He served as Area Chair for ECCV’18, 3DV’19, SCA’18’19, FG’20, ECCV’20. He will serve as Area Chair for CVPR’21, IJCAI’21 and 3DV’20.

Andreas Geiger is a professor at the University of Tübingen and the Tübingen AI Center and head of the department of computer science. Prior to this, he was a visiting professor at ETH Zürich and a group leader at the Max Planck Institute for Intelligent Systems. He studied at KIT, EPFL and MIT, and received his PhD degree in 2013 from KIT. His research interests are at the intersection of computer vision, machine learning and robotics. His work has been recognized with several prizes, including the Longuet-Higgins Prize, the Mark Everingham Prize, the IEEE PAMI Young Investigator Award, the Heinz Maier Leibnitz Prize and the German Pattern Recognition Award. In 2013, 2021 and 2024 he received the CVPR best paper and best paper runner-up awards. He also received the best paper award at GCPR 2015 and 3DV 2015 as well as the best student paper award at 3DV 2017. In 2019, he was awarded an ERC starting grant. He is an ELLIS fellow and coordinates the ELLIS PhD and PostDoc program. He regularly serves as area chair and associate editor for several computer vision conferences and journals including CVPR, ICCV, ECCV, PAMI and IJCV. He maintains the KITTI and KITTI-360 benchmarks.

Marc Toussaint is professor for Intelligent Systems at TU Berlin and was previously professor for ML and Robotics at U Stuttgart, Max Planck Fellow at the MPI for Intelligent Systems, and visiting scholar at MIT. His research interests are in the intersection of AI and robotics, namely in using machine learning, optimization, and AI reasoning to tackle fundamental problems in robotics. Concrete research topics include models and algorithms for physical reasoning, task-and-motion planning (logic-geometric programming), learning heuristics, the planning-as-inference paradigm, and learning to transfer model-based strategies to reactive and adaptive real-world behavior.