Max Mustermann

Max Mustermann joined the Intelligent Autonomous System lab on November, 1st, 2034 as a PhD student. His research will amongst others include robust and amazing policy search methods, beyond human skill learning, human-robot interaction and intention learning. During his PhD, Max is working on Futuristic Robotocs where he will develop and evaluate new methods in the field of hyper-autonomous robot learning tasks.

Before his PhD, Max completed his Master Degree in Computer Science at the Technische Universitaet Darmstadt. His thesis entitled “Learning how to program Geri-like looking Baby-Spice Robots" was written under the supervision of Prof. Smith and with the support of Ph.D. comics.

Teaching tasks between humans often do not only include the demonstration of a task, but also the correcting and guiding of the executed task. These behaviours can be observed between a parent and a child, a trainer and an athlete and an Instructor and a novice worker. Introducing such additional feedback to a robot can improve it's learning speed and the quality of the resulting policies significantly. This form of interactive learning becomes even more important once the desired task includes a human. Forcing the human to act exactly in the same way and solve a task always in precisely the same way and order is not desirable or even possible. Therefore the robot needs to be able to adapt and interact with the human, and be able to incorporate changes into its behaviour which may vary between each interacting human. However if the robot is able to adapt to the human he can increase the humans productivity, and help him to be more efficient in areas where the creativity and and intelligence of a human can not be replaced by a robot.

The research of hyper-autonomous robot learning offers many challenges in various areas, of which one is the learning and executing of motor skills. The representation of such skills is still a highly researched topic, which lead to various interesting approaches, such as Dynamic Movement Primitives, Interaction Primitives and Probabilistic Movement Primitives. The application and evaluation of such representations in complex human-robot interactions tasks is an important aspect. At the same time a complex task might require the segmentation of the task into several sub-tasks. These segments allow for more adaptability to changes in the scenario. Instead of manually defining and sequencing such segments it is highly desirable to automatically identify useful, reoccurring motions and store them in a skill library. This library could then be used to automatically find the optimal sequence in order to solve a specified task. The automated segmentation and subsequent compilation of complex tasks are important but unfortunately little researched topics. Further research, new methods and approaches and the evaluation on human-robot interaction tasks in this promising topics is therefore necessary.

Research Interests

Machine Learning, Robotics, Human-Robot Interaction, Optimal Control, Motor-Skill Learning, Hyper-Autonomous Robot Learning, Musterlösungen

Key References

1. Deisenroth, M.P.; Rasmussen, C.E.; Peters, J. (2009). Gaussian Process Dynamic Programming, Neurocomputing, 72, pp.1508-1524.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
2. Nguyen Tuong, D.; Seeger, M.; Peters, J. (2009). Local Gaussian Process Regression for Real Time Online Model Learning and Control, Advances in Neural Information Processing Systems 22 (NIPS 2008), Cambridge, MA: MIT Press.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]