Projekt: COSPAL - Cognitive Systems using Perception-Action Learning

Forscher: Sommer G. , Siebel N. , Zeitschel S. , Hoppe F. , Prehn H.

[COSPAL Logo] Research project, funded by the European Union under grant IST-2003- - Cognitive Systems

The European Framework 6 Project COSPAL (July 2004 to June 2007) is concerned with the development of a system design for systems which combine perception and action capabilities to solve complex planning and manipulation tasks. Methods using supervised, unsupervised and reinforcement learning are employed. An important focus is also on incremental learning such that the system can adapt to new situations and tasks during its lifetime. The learning methods are based on Artificial Neural Networks (e.g. DCS Networks with online learning capabilities), Associative Networks and others.

For the development and testing of the newly developed methods we are implementing a demonstrator system that solves a shape-sorting puzzle like the ones used as toys. The system has several layers of abstraction, with the highest being a symbolic processing unit. At each layer a Perception-Action Cycle (PAC) can be identified, building a hierarchy of PACs. While traditional methods (or a 3-year old child) could be used to solve the shape-sorting task it is our goal to use only methods that learn how these PACs can be realised.

The task of our group, the Cognitive Systems Group of the Christian-Albrechts-University of Kiel, is to implement learning methods for those parts of the system closest to the robot system hardware. The perception/action modules and controls learned by our group are those for detecting objects and extracting features from images, classifying these features and moving the robot (long range movement, alignment, obstacle avoidance). An emphasis is also placed on learning to imitate human-like movement while solving the task.

More information can be found on the COSPAL project home page.


Several classifiers and function approximation techniques have been devoloped. Below you can find their MATLAB implementation and documentation.

LCC: Local Credibility Criterion Classifier
HyperLCC: Hyper LCC Classifier
HLAM: Hierarchical Network of Locally Arranged Models