The goal of this project is to investigate the principles underlying co-evolution of a body shape and its neural controller. As a specific model system, we consider a robot hand that is controlled by a neural network. In contrast to existing work, we focus on the genetic regulation of neural circuits and morphological development. Our interest is directed at a better understanding of the facilitatory potential of co-evolution for the emergence of complex new functions, the interplay between development and evolution, the response of different genetic architectures to changing environments, as well as the role of important boundary constraints, such as wiring and tissue costs.
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While language provides us with a concise code capturing much of the movement complexity of our mouth, we still lack a comparable representation for the movement of our hands. This project aims to create a database of human hand interaction patterns from a variety of multimodal data sources. An associated goal is to develop methods for the clustering of captured trajectory data into physics-based models of manual interaction. We hope that the resulting database can make a contribution towards a better grounding of control strategies for anthropomorphic robot hands and develop for robotics a similar utility as the WordNet database has for linguistics. 
Manipulation of paper is a rich domain of manual intelligence that we encounter in many daily tasks. The present project attempts to analyse and implement the "web" of visuo-motor coordination skills to endow an anthropomorphic robot hand with the ability to manipulate paper (and paper-like objects) in a variety of situations of increasing complexity. This will include aspects such as modeling interaction with compliant objects, action based representation as well as bimanual coordination to enable object transformations such as tearing and folding.
Unlike most existing approaches to the grasp selection task for anthropomorphic robot hands, this vision-based project aims for a solution, which does not depend on an a-priori known 3D shape of the object. Instead it uses a decomposition of the object view (obtained from mono or stereo cameras) into local, grasping-relevant shape primitives, whose optimal grasp type and approach direction are known or learned beforehand. Based on this decomposition a list of possible grasps can be generated and ordered according to the anticipated overall grasp quality.
