This paper presents a robot imagination system that generates models of objects prior to their perception. This is achieved through a feature inference algorithm that enables computing the fusion of keywords which have never been presented to the robot together previously. In this sense, robot imagination is defined as the robot’s capability of generating feature parameter values of unknown objects by generalizing characteristics from previously presented objects. The system is first trained with visual information paired with semantic object descriptions from which keywords are extracted. Each keyword creates an instance of the learnt object in an n-dimensional feature space. The core concept behind the robot imagination system presented in this paper is the use of statistically fit hyperplanes in the feature space to represent and simultaneously extend the meaning of grounded words. The inference algorithm allows to determine complete solutions in the feature space. Finally, evolutionary algorithms are used to return these numeric values to the real world, completing an inverse semantic process.
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