Robotics and Cyber-Physical Systems

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

Michael J. Black

Michael J. Black

MPI-IS, Scientific Director

Personal Website

Real-Time Systems

Björn Brandenburg is a Max Planck Research Group leader, heading 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

Björn Brandenburg

Björn Brandenburg

MPI-SWS, Faculty

Personal Website

Scalable Learning and Perception

Mario Fritz heads the Scalable Learning and Perception group at the Max Planck Institute for Informatics. Advances in sensor technology and availability of data resources on the Web now give machines a more detailed “picture” of the real world than ever before—but machines are not yet able to acquire the rich semantic understanding that comes easily to humans. To narrow this gap, two of the group’s main research themes are scalable learning, to facilitate the acquisition of large-scale knowledge representations, and scalable inference, to enable reasoning over large output spaces at test time. Progress in this direction will facilitate seamless interaction and information exchange between machines and humans, with applications in information retrieval, robotics, human-machine collaboration, and assisted living. Read more

Mario Fritz

Mario Fritz

MPI-INF, Senior Researcher

Personal Website

Rigorous Software Engineering

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

Rupak Majumdar

Rupak Majumdar

MPI-SWS, Faculty

Personal Website

Foundations of Algorithmic Verification

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

Joel Ouaknine

Joel Ouaknine

MPI-SWS, Faculty

Personal Website

Autonomous Motion

Stefan Schaal is Professor of Computer Science, Neuroscience, and Biomedical Engineering at the University of Southern California, and a founding director of the Max Planck Insitute for Intelligent Systems. He is head of the Autonomous Motion Department, which conducts applied and theoretical research on intelligent systems that can move, perceive, and learn from experiences. Schaal’s research interests include topics of statistical and machine learning, neural networks, computational neuroscience, functional brain imaging, nonlinear dynamics, nonlinear control theory, and biomimetic robotics. He applies his research to problems of artificial and biological motor control and motor learning, focusing on both theoretical investigations and experiments with human subjects and anthropomorphic robot equipment. Read more

Stefan Schaal

Stefan Schaal

MPI-IS, Scientific Director

Personal Website

Empirical Inference

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

Bernhard Schölkopf

Bernhard Schölkopf

MPI-IS, Scientific Director

Personal Website