University of Birmingham > Talks@bham > Artificial Intelligence and Natural Computation seminars > Selection, Fusion and Detection: Case Studies for Memory-based Architectures in Cognitive Robotics

Selection, Fusion and Detection: Case Studies for Memory-based Architectures in Cognitive Robotics

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Research on cognitive robots that learn in interaction as exemplified in the RoboCub@Home competition is original in its aim to develop multi-modal active perception, social and action competence for robots as a truly joint effort. This joint process and the increasing technological complexity of the resulting systems therefore call for novel engineering approaches.

In this talk I will present a recent approach developed and used in current EU research projects, which promotes the use of so-called memory architectures for the engineering of cognitive robotics systems. The approach enhances event-driven architectures such as ROS from Willow Garage by stateful interaction through memory spaces. However, it considers not only technological challenges such as low-coupling between software components to facilitate rapid progress in development but at the same time allows a flexible organization of learning processes around memory spaces. These information spaces provide temporal context, allow for reasoning also on past events, and furthermore facilitate adaptation and learning.

The concepts of the presented approach are exemplified along a number of uses-cases and challenges for building memory-centered systems taken from ongoing collaborative EU integrated projects such as COGNIRON or ITALK . The presented examples span from bottom-up segmentation of multi-modal input data in a tutoring scenario using the iCub humanoid robot, over person anchoring and learning on a mobile robot to an adaptive self-awareness model generally applicable for anomaly detection in event-based systems.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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