Welcome to the websites of the Machine Learning & Robotics Lab, University Stuttgart. This site is meant to link to all the distributed pages related to the MLR lab. Please browse the menu above to access them. Our official university website is http://www.ipvs.uni-stuttgart.de/abteilungen/mlr/.
Meet some of our robots @ Univ. Stuttgart
The founder of MLR, Prof. Dr. Marc Toussaint, moved to TU Berlin in April 2020, since then, the Humans to Robots Motion (HRM) Research Group has the interim leadership of MLR in Stuttgart. You can check out the HRM research projects for possible research topics and collaborations.
The field of Machine Learning (ML) can be understood as a science of learning systems. While the neurosciences and psychology aim to describe mechanisms of learning in humans and animals, Machine Learning aims to develop algorithms that demonstrate the ability to learn from data and improve with experience. Machine Learning has become a central subdiscipline of Artificial Intelligence and utilizes methods from statistical learning theory for efficient data analysis. In particular the great success of ML for data analysis has led to its application in many commercial and scientific applications, e.g., becoming a central tool for the dominant IT companies to exploit their data as well as for applications in bioinformatics and the neurosciences.
The Machine Learning & Robotics Lab aims to push Machine Learning methods towards intelligent real world systems, in particular robots autonomously learning to interact with and manipulate their environment. Unlike standard data analysis methods, the system needs to actively collect data and derive models of the environment that enable goal-directed decision making and planning.
Our research focusses on the combination of decision theory and machine learning, motivated by applications in robotics. The goal are learning systems that are able to reason about their own state of knowledge (e.g., in a Bayesian way) and decide which actions might yield the most informative future data, make them learn even better and eventually solve problems. We address this in the form of Reinforcement Learning, Planning and Active Learning in probabilistic relational domains. Further, a growing focus of our lab are real-world robotic systems, including trajectory optimization and optimal control methods.