Learning to perceive affordances in a framework of developmental embodied cognition

Recently, the aspect of visual perception has been explored in the context of Gibson's concept of affordances in various ways. We focus in this work on the importance of developmental learning and the perceptual cueing for an agent's anticipation of opportunities for interaction, in extension to functional views on visual feature representations. The concept for the incremental learning of complex from basic affordances is presented in relation to learning of specific affordance features. We demonstrate the learning of causal relations between visual cues and associated anticipated interactions by reinforcement learning of predictive perceptual states. The work pursues a recently presented framework for cueing and recognition of affordance-based visual entities that plays an important role in robot control architectures, in analogy to human perception. We experimentally verify the concept within a real world robot scenario by learning predictive features from delayed rewards, and prove that features were selected for their relevance in predicting opportunities for interaction.