A cognitive approach to affordance learning in robotic ecologies

Abstract

The Robotic Ecology vision shares many similarities with the one pursued by the IoT community: The ideal aim on both fronts is that arbitrary combinations of devices should be able to be deployed in unstructured environments, such as those exemplified in a typical household, and there efficiently cooperate to the achievement of complex tasks. While this has the potential to deliver a range of modular and disruptive applications, a pressing and open research question is how to reduce the amount of pre-programming required for their deployment in real world applications. In order to inspire similar advancements within the IoT community, this extended abstract discusses how this goal has been addressed by pioneering the concept of a self-learning robotic ecology within the EU project RUBICON (Robotic UBIquitous Cognitive Network); how such an approach relates to the concept of Affordances at the basis of Gibsons’ theory of ecological psychology; and how it can be used to drive the gradual adaptation of a robotic ecology to changing contexts and evolving requirements.