Learning contextual affordances with an associative neural architecture

Affordances are an effective method to anticipate the effect of actions performed by an agent interacting with objects. In this work, we present a robotic cleaning task using contextual affordances, i.e. an extension of affordances which takes into account the current state. We implement an associative neural architecture for predicting the effect of performed actions with different objects to avoid failed states. Experimental results on a simulated robot environment show that our associative memory is able to learn in short time and predict future states with high accuracy.