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Aesthetic perception and cognition processes are highly individual dynamic processes due to their dependency on the emotional affective state, perceptual analysis, memory, context, and cognitive mastering. Therefore, an aesthetic experience will always be perceived differently for every person and time. With our approach, we research the potential to define an adaptive physical aesthetic experience by the targeted EEG feedback of the perceiver. In a series of three distinct projects, we use generative robotic control (KUKA|prc), a Deep Convolutional Generative Adversarial Network, and electroencephalography (EEG) to create an aesthetic adaptation strategy within a physical parametric output space. Though certain physical limitations apply, the resulting artefact interaction offers the potential to make the aesthetic definition to a certain extend relational to individual perception and cognition processes and, therefore, to some extent adaptive to emotional, contextual, and cultural change over time.

Using Robotics and A.I. to Physically Explore a Space of Aesthetic Possibilities