Zsolt Kira

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Statement | Heterogeneity & Knowledge Sharing | Multi-robot Systems | Single-Robot Learning
Software Released
All Albums | IROS & EpiRob 2007 | Burning Man '06 | Europe '05 | Japan '04 | Nantahala '03
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Knowledge Sharing in
Heterogeneous Teams

My dissertation involved the sharing of knowledge among heterogeneous robots with differing perceptual and motor capabilities.

I investigated multi-robot tasks such as joint mapping and tracking of objects in an outdoor environment as well as search and rescue. We use information theoretic metrics to enable two heterogeneous robots (with some overlap) to effectively communicate. Publications on these are coming soon!

Mapping Grounded Object Properties across
Perceptually Heterogeneous Embodiments

Mappings between Gaussian Mixture Models (corresponding to color properties) between two different robots.

In the first paper involving building of models of robot differences, we have shown that confusion matrices, describing mappings between various object properties (such as color and texture), can be learned using instances from each robot in a shared context. These models describe which properties represent similar object properties across different robots, and can subsequently be used to faciliate knowledge sharing.


Knowledge Transfer

USARSim simulation environment with aerial and ground robot.

In the 2009 IROS paper [3] (as well as upcoming papers), we use the models of robot differences to actually transfer object models between robots. This bootstraps the learning of one robot using experiences from another robot. This can be evidenced by higher learning curves when using transfer learning as opposed to the robot learning by itself.


The Role of Shared Context

Player/Stage environment used during evaluation of behaviors for shared context.


As an initial step towards the overall goal, the first paper published involves proposing to leverage similarity to deal with heterogeneity. Specifically, we show how establishing a physically shared context can be used to learn models of the differences between two robots.  This is similar to the joint attention or gaze following subfields, but in this case the purpose is to ensure a shared context in order to figure out perception differences arising from robot heterogeneity. In the paper, we analyzed the cost and accuracy of several methods for the establishment of the physically shared context with respect to such modeling. We will apply these methods to use information-theoretic measures developed during the candidacy proposal to determine what perceptual differences and similarities exist between two robots.





©2009 Zsolt Kira