A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Context-Dependent Attention System for a Social Robot
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A Behavioral Analysis of Computational Models of Visual Attention
International Journal of Computer Vision
Creating Brain-Like Intelligence
Creating Brain-Like Intelligence
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Traditional robotics has treated the question of learning as a one way process: learning algorithms, especially imitation learning approaches, are based on observing and analysing the environment before carrying out the `learned' action. In such scenarios the learning situation is restricted to uni-directional communication. However, insights into the process of infant learning more and more reveal that interaction plays a major role in transferring relevant information to the learner. For example, in learning situations, the interactive situation has the potential to highlight parts of an action by linguistic and non-linguistic features and thus to guide the attention of the learner to those aspects that the tutor deems relevant for her current state of mind. We argue that learning necessarily needs to be embedded in an interactive situation and that developmental robotics, in order to model engagement in interaction, needs to take the communicative context into account. We further propose that such an approach necessarily needs to take three aspects into account: (1) multi-modal integration at all processing levels (2) derivation of top-down strategies from bottom-up processes and (3) integration of single modules into an interactive system in order to facilite the first two desiderata.