Difference between revisions of "VVV09 Cognitive Architecture Group"

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(Removed brainstorming session due to lack of interest!!)
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* [[iCub Cognitive Architecture]]
 
* [[iCub Cognitive Architecture]]
  
== Brainstorming Session ==
 
On Wednesday morning, say at 10:00am, it would be nice to meet for an hour or two to brainstorm the iCub cognitive architecture with the goal of capturing the insights and ideas of summer school participants on ways the iCub cognitive architecture might be improved.
 
If people are willing to participate in this, and if people feel it would be helpful, we can kick the session off by presenting a brief overview of the cognitive architecture, its neuroscientific and psychological underpinnings, and its potential operation.
 
 
 
Please add you name below if you would be interested in participating.
 
 
 
''Participants'':
 
David
 
  
 
== Demo ==
 
== Demo ==
  
 
Our demo for the last day shows simple object recognition using color histogram matching. A depth-based attentional system designed by the 3D Task Force Action Duo is used to track objects close to the robot's face, and histogram matching is used to determine whether the object has been previously seen. If it has not been seen, it is committed to memory. The image is converted to log polar coordinates before histogram matching to create a foveation effect.
 
Our demo for the last day shows simple object recognition using color histogram matching. A depth-based attentional system designed by the 3D Task Force Action Duo is used to track objects close to the robot's face, and histogram matching is used to determine whether the object has been previously seen. If it has not been seen, it is committed to memory. The image is converted to log polar coordinates before histogram matching to create a foveation effect.

Revision as of 18:39, 28 July 2009

This is a page for those interested in working on the iCub Cognitive Architecture at VVV09.

Overview

The purpose of the iCub Cognitive Architecture is to create a system for cognition on the platform which is consistent with neuroscience. The focus right now for the group is on creating modules for the architecture that have been missing until now:

  • an auto-associative memory
  • a hetero-associative procedural memory

Both the specifications and implementations of these are being addressed by this group.

Group Contributors

  • David
  • Logan
  • Lydia
  • Alberto
  • Federico T.
  • Giovanni
  • Charles
  • Stephane

Code

The current code which has been created for this is available on Google Code. You can check it out here: http://code.google.com/p/icubcogarch/source/checkout

Progress

  • 7/23/09 -- We have an implementation of a color histogram intersection method for pattern matching. This is being folded into a YARP module. In addition, we plan on making a C++ interface for the auto-associative memory so that many different implementations can tap into this specification.
  • 7/24/09 -- The basic functionality of the color histogram implementation of the AAM works now as a YARP module in simulation. A few finishing touches are being done to ensure that the module takes full advantage of things like the Resource Finder. Giovanni has created a template for the AAM, that expands it to take any kind of implementation. Documentation and tackling the application driver are hopefully the next steps.
  • 7/25/09 -- The AAM module is now completed, and demoable. We changed the algorithm so that it converts the images to log polar coordinates before comparison, so that the image is more 'foveated'. With this there is an obvious improvement in performance.
  • 7/28/09 -- An outline specification of the procedural memory is now available. There are still some open issues and incomplete sections but these will be cleared up either later today or tomorrow. In addition, documentation and YARP module standards compliance is almost complete. We are testing compilation on other platforms.

Important Links


Demo

Our demo for the last day shows simple object recognition using color histogram matching. A depth-based attentional system designed by the 3D Task Force Action Duo is used to track objects close to the robot's face, and histogram matching is used to determine whether the object has been previously seen. If it has not been seen, it is committed to memory. The image is converted to log polar coordinates before histogram matching to create a foveation effect.