Max Planck Institute for Intelligent Systems, Tübingen site

Max Planck Institute for Intelligent Systems, Tübingen site

Intelligent systems can optimise their structure and properties in order to successfully function within a complex, partially changing environment. Three sub-areas – perception, learning and action – can be differentiated here. The scientists at the Max Planck Institute for Intelligent Systems are carrying out basic research and development of intelligent systems in all three sub-areas. Research expertise in the areas of computer science, material science and biology is brought together in one Institute, at two different sites. Machine learning, image recognition, robotics and biological systems will be investigated in Tübingen, while so-called learning material systems, micro- and nanorobitics, as well as self-organisation will be explored in Stuttgart. Although the focus is on basic research, the Institute has a high potential for practical applications in, among other areas, robotics, medical technology, and innovative technologies based on new materials.


Max-Planck-Ring 4
72076 Tübingen
Phone: +49 7071 601-1700

PhD opportunities

This institute has an International Max Planck Research School (IMPRS):

IMPRS for Intelligent Systems

In addition, there is the possibility of individual doctoral research. Please contact the directors or research group leaders at the Institute.

a side-face stone sculpture of Minerva (roughly five meters high) on the left side of the glass entrance of an office building

The cooperation strengthens application-related research on artificial intelligence in Germany


Meshcapade licenses technologies for creating avatars


How fast the development from assisted to fully automated vehicles will progress is uncertain. One crucial factor here is the reliability with which a vehicle can navigate in its surroundings and react to unforeseeable incidents. Our group at the Max Planck Institute for Intelligent Systems showed that methods for motion analysis based on deep neural networks – likely components in future autonomous vehicles – can be confused by small patterns designed to “attack” these networks.


Max Planck Institute for Intelligent Systems and ETH Zurich strengthen cooperation

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Robots can already assist humans with some everyday tasks. But they are out of their depth when faced with unfamiliar environments or even small deviations in the tasks they are trained to perform. To help them learn to adapt more quickly to new circumstances, Michael Mühlebach and Jörg Stückler’s research groups at the Max Planck Institute for Intelligent Systems in Tübingen are developing new training methods for the machines. Their robots even have to prove themselves by engaging in table tennis or body flight.

Artificial intelligence (AI) has long been able to recognize patterns much better than humans can. However, in order to truly be worthy of its name, it would also need to understand causal relationships. And that is precisely what researchers at the Max Planck Institute for Intelligent Systems in Tuebingen are working on.

In the future, it will be more and more common for computers to make decisions about human beings – whether they are granting loans or assessing applicants. However, it happens occasionally that the automated systems that are already in use discriminate against certain groups of people. Niki Kilbertus and Bernhard Schölkopf, researchers at the Max Planck Institute for Intelligent Systems in Tuebingen, want to change this by developing fair algorithms.

As domestic help, healthcare assistants or emergency response units: robots are suitable for these jobs only if they are capable of learning and acting independently, at least to a certain extent. Stefan Schaal and the members of his Autonomous Motion Department at the Max Planck Institute for Intelligent Systems in Tübingen are teaching machines to become flexible and autonomous.

A time may yet come when everyone has their own chauffeur-driven car – if robots take the wheel, that is. In order for autonomous vehicles to become a reality without huge technical outlay, however, computers will have to be able to assess complex traffic situations at least as well as drivers do. Andreas Geiger and his team at the Max Planck Institute for Intelligent Systems in Tübingen are working to develop the necessary software.

The life of an avatar is dependent on technology, including even the very act of its birth. For the virtual figure to look true to life and move realistically in its computer world, its creators need to have detailed information about the body of the real-life model, as well as about its movement. This is precisely the data that the first four-dimensional full-body scanner provides. This device was developed by Michael J. Black, Director at the Max Planck Institute for Intelligent Systems in Tübingen, together with American company 3dMD. With 22 stereo cameras and 22 color cameras taking 60 images per second, the scanner captures a person in various positions and activities that Javier Romero, a scientist at the institute, demonstrates here. For the scan, red and blue squares are printed on Nick Schill, a professional model, and then illuminated with a quickly pulsating spot pattern. The two patterns help the researchers reconstruct the three-dimensional surface of the body and the skin naturally. Not only can this method be used to create true-to-life figures for computer games and films, but it also offers interesting perspectives for research in psychology and medicine. In this way, it will soon be possible to use the realistic avatars in conducting perception experiments on body awareness– for instance to prevent eating disorders.

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Computer vision research unveils stereotypes about body shapes 

2021 María Alejandra Quirós-Ramírez, Stephan Streuber, Michael J. Black

Computer Science

Scientists at the Max Planck Institute for Intelligent Systems show how people perceive others and unconsciously attribute certain character traits to a person based solely on their body shape. They also found that the political affiliation of the observer influences the social perception. The judgment of others depends on both the body shape of the person being observed and the viewer’s own political preference, potentially having political and social implications. The research project aims to raise awareness of this bias. 


Progress in the field of Brain-Computer Interaction: scientists take BCI research out of the lab and into the real world

2020 Matthias R. Hohmann, Lisa Konieczny, Michelle Hackl, Brian Wirth, Talha Zaman, Raffi Enficiaud, Moritz Grosse-Wentrup und Bernhard Schölkopf

Computer Science Neurosciences

Scientists at the Max Planck Institute for Intelligent Systems in Tübingen and the University of Vienna have introduced MYND, an open-source software that allows people to participate in brain-computer interaction (BCI) research from home, without expert supervision. Their research could take the field a decisive step forward: MYND can complement laboratory-based basic research with human-computer interaction experiments in a range of real life environments. The researchers are confident their approach will provide a viable basis for further research on accessible use of BCI in daily life.


Colour patch could throw self-driving vehicles off track

2019 Anurag Ranjan, Joel Janai, Andreas Geiger, Michael J. Black

Computer Science

In our team of researchers at the Max-Planck-Institute for Intelligent Systems in Tübingen we show that optical flow systems based on deep neural networks – a likely component of future autonomous cars – are vulnerable to adversarial attacks. The computer vision experts are shaking up the automotive industry by warning car manufacturers around the globe that it could take a simple color pattern to put the brakes on computer vision systems in autonomous cars.


Robots with their own thirst for action

2017 Georg Martius

Computer Science Material Sciences

Robots as helpers in everyday life can make our lives better in the future. However, there is much research needed to get there. One problem is the hardware. It needs to withstand everyday usage without being bulky or dangerous. The bigger problem, however, is to develop the right "brain”. To come somewhere close to human skills, a robot has to learn a lot by itself. The researchers of the Autonomous Learning Group at MPI for Intelligent Systems are working on artificial curiosity and the associated learning methods so that artificial systems can improve themselves in the future.


Computing with Uncertainty

2016 Hennig, Philipp

Computer Science

Machine learning requires computer hardware to reliable and efficiently compute estimations for ever more complex and fundamentally incomputable quantities. A research team at MPI for Intelligent Systems in Tübingen develops new algorithms which purposely lower the precision of computations and return an explicit measure of uncertainty over the correct result alongside the estimate. Doing so allows for more flexible management of resources, and increases the reliability of intelligent systems.

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