Max Planck Research Group for Autonomous Vision
We are interested in computer vision and machine learning with a focus on 3D scene understanding, parsing, reconstruction, material and motion estimation for autonomous intelligent systems such as self-driving cars or household robots. In particular, we investigate how complex prior knowledge can be incorporated into computer vision algorithms for making them robust to variations in our complex 3D world. You can follow us on GoogleScholar (paper email alert), on YouTube (video email alert) and on Facebook. Pictures from recent group activities can be found in our gallery!
Max Planck Research Group for Autonomous Learning
We are interested in autonomous learning, that is how an embodied agent can determine what to learn, how to learn, and how to judge the learning success. In particular, we focus on learning to control a robotic body in a developmental fashion. Artificial intrinsic motivations are a central component that we develop using information theory and dynamical systems theory. We work on reinforcement learning, representation learning, and internal model learning.
We conduct research to understand neurocontrol and mechanical principles of dynamic legged locomotion in animals, by designing and applying running legged robots and their computational models.
We are interested in the underlying mechanisms of dynamic legged locomotion in animals. As models we are designing and applying legged robots and their computational counterparts.
We test blueprints from animal functional morphology, neurocontrol, and general biomechanics. Our legged robots are applied as research platforms that produce rich, high-dimensional experimental data under realistic conditions. We can cross-check the gathered data with biomechanical data of running animals. This eventually allows us to identify individual components, and functional relations between components.
Our work spans from bioinspired and biomimicking robot locomotion, bioinspired approaches to sensor design, learning locomotion, to understanding locomotion biomechanics in animals and robotic machines.
Our group has broad interests in the interaction of optical, electric, and magnetic fields with matter at small length scales. We work on new 3-D fabrication methods, self-assembly, actuation, and propulsion. We have observed a number of fundamental effects and are developing new experimental techniques and instruments.
The Independent Max Planck Research Group on Probabilistic Numerics
Numerical Problems --- linear algebra and optimization, integration and the solution of differential equations --- are the computational bottleneck of artificial intelligent systems. Intriguingly, the numerical algorithms used for these tasks are also compact little intelligent agents themselves. They estimate unknown / uncomputable quantities by observing the result of feasible computations. They also actively decide which computations to perform.
The Research Group on Probabilistic Numerics studies this philosophical and mathematical connection between computation and inference. We aim to build a theoretical understanding of numerical computer algorithms as agents acting rationally under uncertainty. We analyse existing algorithms from this viewpoint, and propose novel algorithms that provide functionality for key computational challenges in the science of Intelligent Systems.
Max Planck Fellow Group
We work on the theoretical analysis of machine learning algorithms. Our current focus is on comparison-based learning algorithms and on algorithms on random graphs and networks. The group is lead by Ulrike von Luxburg, the funding comes from a Max Planck Fellowship.
The groups by Ulrike von Luxburg are distributed between the Max Planck Institue and the University of Tübingen, our main webpage is the one at the university .
The Max Planck branch of our group consists of the following people: