Robots and other complex cyber-physical systems (CPS) sense, process, and react to information from the physical world. They must operate safely even in the presence of uncertainties and resource constraints. To enable advanced robotics and CPS applications, research in this area tackles a wide range of issues including visual perception, inference from empirical data, motor learning and control, and the design, implementation, and verification of safe and performant CPS.
Groups and Researchers in this Field
Perceiving Systems
Michael J. Black is one of the founding directors of the Max Planck Institute for Intelligent Systems, where he leads the Perceiving Systems Department. His research addresses a variety of topics relating to computer vision and perception: the statistics of natural scenes and their motion; articulated human motion pose estimation and tracking; the estimation of human body shape from images and video; the representation and detection of motion discontinuities; and the estimation of optical flow. His early work on optical flow has been widely used in Hollywood films. He also does research on neural engineering for brain-machine interfaces and neural prostheses. He is an honorary professor at the University of Tübingen, visiting professor at ETH Zürich, and adjunct professor (research) at Brown University. Read more
Björn Brandenburg leads the Real-Time Systems group at the Max Planck Institute for Software Systems. His main research interests are real-time systems, operating systems, synchronization protocols, and embedded systems, with a focus on the design and implementation of systems that are robust, efficient, and amenable to a priori analysis. To this end, the group engages in both systems building and the development of novel analysis methods. As a result of his work with OS kernels, he is also interested in the construction, testing, validation, and performance evaluation of operating systems and other complex systems software. Read more
Christoph Keplinger is a director at the Max Planck Institute for Intelligent Systems (MPI-IS) in Stuttgart, Germany, where he heads the Robotic Materials Department. Building upon his background in soft matter physics (PhD, Johannes Kepler University Linz, Austria), mechanics and chemistry (Postdoc, Harvard University, USA), he leads a highly interdisciplinary research group at MPI-IS, with a current focus on (I) soft robotics, (II) energy capture and (III) functional polymers. He is the principal inventor of HASEL artificial muscles, a new technology that will help enable a new generation of lifelike robotic hardware; in 2018 he co-founded Artimus Robotics to commercialize the HASEL technology, and has served as Chief Science Officer (CSO) of Artimus since its founding. Read more
Katherine Kuchenbecker is a director at the Max Planck Institute for Intelligent Systems, where she leads the Haptic Intelligence Department, which seeks to endow robots with astute haptic perception and invent methods for delivering realistic haptic feedback to users of telerobotic and virtual reality systems. Dr. Kuchenbecker’s research addresses the sensing, understanding, and display of tactile information for robots, teleoperation, and innovative interfaces. Her work combines inspiration from neuroscience with novel materials, machine learning, and robotic systems to uncover the principles that are central for haptic perception. Read more
Rupak Majumdar is a Scientific Director at the Max Planck Institute for Software Systems, where he leads the Rigorous Software Engineering group. His main research interests include verification and control of reactive, real-time, hybrid, and probabilistic systems, software verification and programming languages, logic, and automata theory. His group investigates both foundational principles and practical tools for the design and analysis of computer systems. Some recent research directions have included methodologies and tools for the automated co-design of embedded controllers and their implementations, foundations of robustness for hybrid systems, scalable tools for coverability analysis of Petri nets, algorithms for the analysis of infinite-state systems, and verification of asynchronous programs. Read more
Michael Muehlebach is leading the independent research group Learning and Dynamical Systems at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. He is interested in a wide variety of subjects, including machine learning, dynamics, control, and optimization. Some of his recent work deals with large-scale constrained optimization in a machine learning context, stochastic minimax optimization, adaptive decision-making and reinforcement learning. While rigorous theory and mathematical analysis forms the basis of his research, he also likes to evaluate his algorithms in experiments with real-world cyber-physical or robotic systems, such as balancing robots or flying vehicles. Throughout his career Michael has received numerous awards, such as the ETH Medal and the HILTI prize for innovative research, and he was awarded the Branco Weiss and Emmy Noether Fellowship. Read more
Joel Ouaknine is a Scientific Director at the Max Planck Institute for Software Systems, where he leads the Foundations of Algorithmic Verification group. He also holds secondary appointments at Saarland University and Oxford University. His research interests span a range of topics broadly connected to algorithmic verification and theoretical computer science. His group's recent focus has been on decision and synthesis problems for linear dynamical systems (both continuous and discrete), making use among others of tools from number theory, Diophantine geometry, and real algebraic geometry. Other interests include the algorithmic analysis of real-time, probabilistic, and infinite-state systems (e.g. model-checking algorithms, synthesis problems, complexity), logic and applications to verification, automated software analysis, and concurrency. Read more
Dr. Klaas P. Prüssmann leads the Magnetic Resonance Imaging group at the Max Planck Institute for Intelligent Systems. The group's research goals aim in two directions. The first involves working towards MR-guided micro-robotics. In MRI, magnetic fields serve to elicit and encode nuclear magnetic resonance while micro-robotics use magnetism for mechanical actuation and navigation. We are exploring the resulting synergies in collaboration with the Department for Physical Intelligence. Secondly, we are interested in merging the latest in MR technology and image reconstruction with current advances in empirical inference. Read more
Anne-Kathrin Schmuck is an independent research group leader at the Max Planck Institute for Software Systems in Kaiserslautern, Germany, funded by the Emmy Noether Programme of the German Science Foundation (DFG). Her research is driven by the goal to significantly expand the applicability of fully automated synthesis tools to reliable cyber physical system design. Her work draws inspiration from both Control Theory and Computer Science and centers around Reactive Synthesis, Supervisory Control Theory, Abstraction-Based Controller Synthesis, and Hierarchical Control. Recently, she has developed lazy abstraction techniques where abstract automata models of continuous system dynamics are generated lazily to enable efficient controller synthesis for discrete mission tasks. Further, she is developing contract-based distributed synthesis methods to safely coordinate multiple system components. Read more
Bernhard Schölkopf directs the Empirical Inference Department at the Max Planck Institute for Intelligent Systems. The department investigates problems of empirical inference, i.e. inference based on empirical data. The type of inference can vary, including for instance inductive learning (estimation of models such as functional dependencies that generalize to novel data sampled from the same underlying distribution), or the inference of causal structures from statistical data (leading to models that provide insight into the underlying mechanisms, and make predictions about the effect of interventions). Empirical data may also vary, from sparse experimental measurements (e.g. microarray data) to visual patterns. The department uses theoretical, algorithmic, and experimental approaches to study these problems. Read more
