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.

  

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