Creating autonomous robots that can learn to assist humans in
situations of daily life is a fascinating challenge for machine
learning. While this aim has been a long-standing vision of
artificial intelligence and the cognitive sciences, we have yet
to achieve the first step of creating robots that can learn to
accomplish many different tasks triggered by environmental
context or higher-level instruction. The goal of our robot learning
laboratory is the realization of a
general approach to motor skill learning, to get closer towards
human-like performance in robotics. We focus on the solution
of fundamental problems in robotics while developing machine-learning methods.
Computational Learning for Autonomous Systems (CLAS)
The new group on Computational Learning for Autonomous Systems Group is headed by Gerhard Neumann, who is Assistant Professor at the TU-Darmstadt since September 2014. The main focus of the CLAS group is to investigate computational learning algorithms that allow artificial agents to autonomously learn new skills from interaction with the environment, humans or other agents. We believe that such autonomously learning agents will have a great impact in many areas of everyday life, for example, autonomous robots for helping in the household, care of the elderly or the disposal of dangerous goods.
An autonomously learning agent has to acquire a rich set of different behaviours
to achieve a variety of goals. The agent has to learn autonomously how to explore its environment and
determine which are the important features that need to be considered for making a decision.
It has to identify relevant behaviours and needs to determine
when to learn new behaviours. Furthermore,
it needs to learn what are relevant goals and how to re-use behaviours in order to achieve new goals.
In order to achieve these objectives, our research concentrates on
hierarchical learning and structured learning of robot control policies, information-theoretic methods for policy search, imitation learning and autonomous exploration, learning forward models for long-term predictions, autonomous cooperative systems and multi-agent systems and the biological aspects of autonomous learning systems.
In case that you are searching for our address or for directions on how to get to our lab, look at our contact information. We always have thesis opportunities for enthusiastic and driven Masters/Bachelors students (please contact Jan Peters or Gerhard Neumann). Check out the currently offered theses (Abschlussarbeiten) or suggest one yourself, drop us a line by email or simply drop by! We also occasionally have open Ph.D. or Post-Doc positions, see OpenPositions.
Ewerton, M.; Neumann, G.; Lioutikov, R.; Ben Amor, H.; Peters, J.; Maeda, G. (2015). Learning Multiple Collaborative Tasks with a Mixture of Interaction Primitives, Proceedings of the International Conference on Robotics and Automation (ICRA).
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Gerhard Neumann will co-organize a Workshop with Heni Ben Amor and Neil Dantam on Policy Representations for Humanoid Robots at the IEEE-RAS International Conference on Humanoid Robots (Humanoids 2014).