University of Birmingham > Talks@bham > Computer Science Departmental Series > Towards A Knowledge Representation and Reasoning Architecture for Human-Robot Collaboration

Towards A Knowledge Representation and Reasoning Architecture for Human-Robot Collaboration

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If you have a question about this talk, please contact Mohammad Tayarani.

Host: Jeremy L Wyatt

Robots deployed to collaborate with humans in homes, offices, and other domains, have to represent knowledge and reason at both the sensorimotor level and the cognitive level. One fundamental challenge is that of representing, revising, and reasoning with qualitative and quantitative descriptions of uncertainty and incomplete domain knowledge obtained from different sources. Towards addressing this challenge, this talk will describe an architecture that tightly couples the commonsense reasoning capabilities of declarative programming with the uncertainty modeling capabilities of probabilistic graphical models. I shall also briefly describe probabilistic algorithms that use these representation and reasoning capabilities to guide the learning of object models based on visual context and appearance cues. If time permits, I shall illustrate the use of these algorithms for estimation tasks in non-robotics application domains.

This talk is part of the Computer Science Departmental Series series.

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