"The ability to perceive and understand dexterous object manipulation from visual information, irrespective of an object’s physical characteristics, is critical for next generation robotic systems. It enables fluent interaction outside the highly constrained environments of today’s applications and allows robots to enter domestic settings and natural environments. Many approaches have been proposed to this end, but to enable real-time performance, all of them need to severely restrict model detail, degrees of freedom of articulated objects, and/or deformation complexity. The real-time aspect is critical since the perception of deformable objects under manipulation seems to be a dynamic process, in which specific actions may be efficiently used as active exploration primitives towards model abstraction. This requires closed-loop perception-action cycles. In this project, the candidate will develop a theoretical communication framework that removes a critical obstacle in current systems: the sensory/semantic communication bottleneck. In accordance with biological vision systems, the feedback and feedforward communication between dense low level sensory and compact high level semantic representations will be shaped using visual attention methods. An internal model representation will be developed that supports varying degrees of invariance to an object’s physical characteristics, and the action state space will be reduced by considering objects together with their manipulators. The candidate will also realize the communication framework as a real-time hybrid architecture by combining conventional (CPUs) with graphics processing units (GPUs).By providing multiple relevant contributions across the spectrum of the FP7 objectives in terms of its potential to advance robotic manufacturing, medical image processing, and computing paradigms, this project will enable the candidate to maintain and enhance his position at the forefront of advances in this field."