Learning (WP5)

This work package researches novel learning algorithms which arise from the context of rich motor skills and the hard learning problem introduced in the experimental scenarios. The research in this work package is however of a more theoretical nature and the dissemination of the results will happen to the broader machine learning community. WP6 combines the results from this WP5 in an architecture which will be used in the actual robotic experiments. The following objectives apply:

• Development of new learning algorithms and the redesign of existing learning algorithms for recurrent neural networks, especially exploiting reservoir computing methods.
• Integration of different learning algorithms in complex modular control architectures.
• Development of learning algorithms that operate in interaction with a human caretaker, by scaffolding and imitation.
• Development of cognitive-level learning paradigms supporting agent autonomy, especially for autonomous discovery and meta-learning.

Leading institute: Graz University of Technology