Katharina Muelling joined the Max-Planck Institute for Biological Cybernetics in 2007 as an undergraduate student in the Empirical Inference Department, supervised by Jan Peters. At the same time, she studied Bioinformatics at the Eberhard Karls University of Tuebingen (altogether from 2003 to 2009), and graduating with a Diplom-Informatiker degree (German integrated Masters and Bachelor) in 2009. Please see her curriculum vitae for more biographical information.
Katharina became a Ph.D. student in December 2009. Her work focusses on motor control and learning in complex motor tasks such as table tennis. Table tennis is ideal for studying complex motor skills as it requires fast movements, accurate control, adaptation to new parameters and is based on several elemental movements. In this context, she is interested both in human motor control as well as synthetic robotics approaches.
Research Interests: Robotics, Computational Models of Human Motor Control, Robot Learning Architectures, Inverse Reinforcement Learning, Learning by Demonstration
Biographical Information: Please see her curriculum vitae.
Publications: Please see her publications .
Collaborators: Jens Kober, Oliver Kroemer, Zhikun Wang, Abdeslam Boularias, Betty Mohler, Jan Peters
Extracting strategic information
The goal of this project is to develop a Markov Decision Process (MDP) framework for table tennis in order to get insights into the strategies employed by humans in this game. Therefore, an appropriate reward functon that describes the goal of the game is essential. We want to apply inverse reinforcement learning approaches (IRL) to infer the reward function from human motion capture data.
Towards Learning Robot Table Tennis
Autonomously learning new motor tasks from physical interactions is an important goal for both robotics and machine learning. However, when moving beyond basic skills, most monolithic machine learning approaches fail to scale. For more complex skills, methods that are tailored for the domain of skill learning are needed. In this project, we present a new framework that enables a robot to learn basic cooperative table tennis from demonstration and interaction with a human player. To achieve this goal, we created an initial movement library from kinesthetic teach-in and imitation learning. The movements stored in the movement library can be selected and generalized using the proposed mixture of motor primitives algorithm. As a result, we obtain a task policy that is composed of several motor primitives weighted by their ability to generate successful movements in the given task context. These weights are computed by a gating network and can be updated autonomously.
Key Reference: Muelling, K.; Kober, J.; Kroemer, O.; Peters, J. (2013). Learning to Select and Generalize Striking Movements in Robot Table Tennis, International Journal of Robotics Research, 32, 3, pp.263-279. See Details [Details] Download Article [PDF] BibTeX Reference [BibTex]
Biomimetic Robot Table Tennis Player
Playing table tennis is a difficult motor task that require fast movements, accurate control and adaptation to task parameters. Although human beings see and move slower than most robot systems, they significantly outperform all table tennis robots. One important reason for this higher performance is the human movement generation. In this project, we study human movements during a table tennis match and present a robot system that mimics human striking behavior. Our focus lies on generating hitting motions capable of adapting to variations in environmental conditions, such as changes in ball speed and position.
Key Reference: Muelling, K.; Kober, J.; Peters, J. (2011). A Biomimetic Approach to Robot Table Tennis, Adaptive Behavior Journal, 19, 5. See Details [Details] Download Article [PDF] BibTeX Reference [BibTex]
Mail: Katharina Muelling, Spemannstr. 38, 72076 Tuebingen, Germany
email muelling [at] tuebingen [dot] mpg [dot] de