Technische Universität München Robotics and Embedded Systems



Emergence of Cognitive Grasping through Introspection, Emulation and Surprise

GRASP is an Integrated Project funded by the European Commission through its Cognition Unit under the Information Society Technologies of the seventh Framework Programme (FP7). The project was launched on 1st of March 2008 and will run for a total of 48 months.

The aim of GRASP is the design of a cognitive system capable of performing grasping and manipulation tasks in open-ended environments, dealing with novelty, uncertainty and unforeseen situations. To meet the aim of the project, studying the problem of object manipulation and grasping will provide a theoretical and measurable basis for system design that is valid in both human and artificial systems. This is of utmost importance for the design of artificial cognitive systems that are to be deployed in real environments and interact with humans and other agents. Such systems need the ability to exploit the innate knowledge and self-understanding to gradually develop cognitive capabilities. To demonstrate the feasibility of our approach, we will instantiate, implement and evaluate our theories and hypotheses on robot systems with different embodiments and complexity.

GRASP goes beyond the classical perceive-act or act-perceive approach and implements a predict-act-perceive paradigm that originates from findings of human brain research and results of mental training in humans where the self-knowledge is retrieved through different emulation principles. The knowledge of grasping in humans can be used to provide the initial model of the grasping process that then has to be grounded through introspection to the specific embodiment. To achieve open-ended cognitive behaviour, we use surprise to steer the generation of grasping knowledge and modelling.




[1] Chavdar Papazov, Sami Haddadin, Sven Parusel, Kai Krieger, and Darius Burschka. Rigid 3D Geometry Matching for Grasping of Known Objects in Cluttered Scenes. International Journal of Robotics Research, 31, April 2012. [ .bib | .pdf ]
[2] Chavdar Papazov and Darius Burschka. Stochastic Global Optimization for Robust Point Set Registration. Computer Vision and Image Understanding, 115, December 2011. [ DOI | .bib | .pdf ]
[3] Chavdar Papazov and Darius Burschka. Deformable 3D Shape Registration Based on Local Similarity Transforms. Computer Graphics Forum, 30, 2011. (special issue SGP'11). [ .bib | .pdf ]
[4] Chavdar Papazov and Darius Burschka. An Efficient RANSAC for 3D Object Recognition in Noisy and Occluded Scenes. In Proceedings of the 10th Asian Conference on Computer Vision (ACCV'10), November 2010. (oral presentation; acceptance rate: 5%). [ .bib | .pdf ]
[5] Chavdar Papazov and Darius Burschka. Stochastic Optimization for Rigid Point Set Registration. In Proceedings of the 5th International Symposium on Visual Computing (ISVC'09), December 2009. (oral presentation). [ .bib | .pdf ]