Welcome to the ALR-Lab
This site is still under construction
The Autonomous Learning Robots (ALR) Lab at the Institute for Anthropomatics and Robotics of the Department of Informatics , focuses on the development of novel machine learning methods for robotics. Future robot technology will have to deal with very challenging real world scenarios that are quite different from the lab environments typically considered in robotics research. Real world environments are unknown and unstructured, consisting of objects of unpredictable shapes or even other, unknown agents such as humans. The robot can encounter so many different situations while interacting with such environments that pre-programming such tasks seems to be infeasible.
Our research is focused on the intersection of machine learning, robotics, human-robot interaction and computer vision. Our goal is to create data-efficient and mathematically principled machine learning algorithms that are suitable for complex robot domains such as grasping and manipulation, forceful interactions or dynamic motor tasks. In our research, we always aim for a strong theoretical basis for our developed algorithms which are derived from first order principles. In terms of methods, our work is is focused on:
- Reinforcement Learning and Policy Search
- Imitation Learning
- Movement Representations
- Time-Series Modelling
While we thrive to extend the state of the art for each of these areas of machine learning, our vision is to create an orchestration of these methods in order to develop a fully autonomous learning robotics system.