Max Planck Research Group for Autonomous Vision
Our group is co-located at the University of Tübingen and the MPI for Intelligent Systems in Tübingen. 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 our Research Blog (blog email alert), GoogleS... Read More
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 investigate principles of biomechanics and control of dynamic locomotion, in legged animals and robots.
We are extending research in biomechanics and neurocontrol by implementing and controlling custom designed, legged robots, and their simulated models. We see dynamic legged locomotion as the product of a tightly interconnected and adapted motor control, sensing, and mechanical system. Understanding the underlying principles that enable animals with limited control bandwidth to achieve both agile and robust locomotion is an exemplary key question in legged locomotion. Research at the Dynamic Locomotion Group focuses on applying legged robots and their models to provide biomechanically relevant locomotion data. This allows us to qualitatively and quantitatively analyze and compare legged locomotion, in robots and animals. We are interested in testing and applying both existing locomotion control concepts, and learning new concepts of locomotion control. We are especially interested in bip... Read More
Max Planck Independent Research Group for Embodied Vision
We research fundamentals of intelligent embodied agents such as robots that learn to perceive and act through interaction with their environment. Our group investigates novel methods for learning the basic physical 3D understanding of dynamic environments up to complex tasks such as autonomous navigation and object manipulation from raw sensory measurements and environment interactions. Besides vision as a primary sensing modality, we also consider further sensing modalities such as tactile or proprioceptive sensing.
The independent Max Planck Research Group on Intelligent Control Systems.
Research in the Intelligent Control Systems group focuses on decision making, control, and learning for autonomous intelligent systems. We develop fundamental methods and algorithms that enable robots and other intelligent systems to interact with their environment through feedback, autonomously learn from data, and interconnect with each other to form collaborative networks. Turning mathematical and theoretical insight into enhanced autonomy and performance of real-world physical systems is an important and driving facet of our work. The Intelligent Control Systems group is an independent Max Planck Research Group at MPI-IS Stuttgart primarily funded through the Cyber Valley Initiative.
We study how animals move using non-invasive, state of the art, motion observation techniques (e.g. high speed video, pose estimation based on deep learning) both in their natural habitat and in the lab in cooperation with academic partners around the world. To better understand biological movement as observed first hand in natural systems through original discovery, we also build biorobotic models that emulate animal locomotion. Bodies are in nature are soft, and so our approach leverages advances in Soft Robotics. In this spirit, we make diverse soft ‘artificial muscles’ (actuators) and soft sensors. Discovery of principles of animal locomotion can then be transferred to biologically informed robots where advantageous. These robophysical models therefore serve as instruments of knowledge. Robotics-inspired Biology can help us better understand the spectacular and graceful movement observed in natural systems.
Locomotion in Biorobotic & Somatic Systems is an independent Max Planck Research Group at the interdisciplinary Max Planck Institute for Intelligent Systems (formerly Metals and Materials Science). Leveraging expertise in diverse fields as biology and robotics, we explore the behavioural and morphological adaptations of natural systems, to achieve robust multimodal locomotion. Richard Feynman famously said: “What I cannot create, I do not understand”. That is, if we cannot recreate how animals move, we have not understood precisely how it works. Curiosity driven fundamental research is therefore required to decipher how animals are able to gracefully navigate irregular terrain with such agility and robustness. The past decade has presented a dramatic expansion in the development of mobile robots and the application of robotic systems to practical tasks. Despite the proliferation of computation and sensing at the small scale, robots still remain largely unable to a... Read More
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.
Welcome to the Movement Generation and Control Group website
What are the algorithmic principles that would allow a robot to run through a rocky terrain, lift a couch while reaching for an object that rolled under it or manipulate a screwdriver while balancing on top of a ladder? By answering these questions, we try to understand the fundamental principles for robot locomotion and manipulation that will endow robots with the robustness and adaptability necessary to efficiently and autonomously act in an unknown and changing environment.
The Independent Max Planck Research Group on Probabilistic Numerics
We have moved! As of 1 October 2018, our group transformed into the Chair for the Methods of Machine Learning at the University of Tübingen. Please check out our new webpage there. 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 co... Read More
We combine psychology and artificial intelligence to help people reach their goals and improve their minds.
Welcome to the website of the Max Planck Research Group for Rationality Enhancement! The scientific mission of the Group is to lay the cognitive and technological foundations for helping people become more effective. Our two-pronged approach synergistically combines basic research on the computational mechanisms of human learning and decision-making with the development of intelligent systems for enhancing human rationality. Our basic research focuses on how people learn how to decide, decision strategies that make optimal use of limited time and bounded cognitive resources, cognitive control, planning, and goal-setting. Our applied research translates the resulting theories and computational models into cognitive tutors that teach people how to make better decisions and cognitive prostheses that augment people's limited cognitive capacities with artificial intelligence. We evaluate the models, cognitive tutors, and cognitive prostheses we develop i... Read More
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 nanometer scale is where the chemistry, biology, and materials sciences converge. The optical properties of metal nanoparticles have been an object of fascination since ancient times. When light interacts with a metal nanoparticle (for example a gold colloid in a stained church window), collective oscillations of conduction electrons known as particle plasmons are excited.