Konzeptionelle Abstraktion bei Menschen und Robotern (ABSTRACTRON)

Funding type: Research Südtirol/Alto Adige – 2022

Duration: 30/06/2024 – 31/08/2025

Everyday concepts, such as the ability of a glass to hold a liquid, or a plate to support the cake, are learnt by children effortlessly in early childhood through simple interactions with the physical world, everyday activities within the environment they encounter and learn to understand. Bringing this level of effortless learning to artificial systems, both physical (robots) and non-physical (software artefacts) is one of the big challenges of current efforts in Artificial Intelligence (AI).  

This trend of research, known as the cognitive turn in AI, is in full swing, although significant challenges are raised by a lack of understanding of how to analyse formally some of the key concepts of cognition enabling such learning processes.  A close relationship is hypothesised in cognitive science between image schemas, i.e. simple yet abstract notions (such as containment and support) which are learnt in the earliest phases of human conceptual development and bootstrapping higher conceptual thinking and metaphoric thought, and affordances, i.e. the potential actions on objects in an environment (such as putting the cake on the plate). 

An understanding of these notions, for artificial agents and humans,  depends deeply on an understanding of how change of spatial conditions impacts the affordances of objects or agents in a given environment. 

A focal point of this project, therefore, lies at the intersection of embodied cognition, the ontology of affordance and image schema, and knowledge-enhanced frameworks in robotics: here, the whole pipeline from an encoding of innate knowledge of physics, through event or activity recognition and interpretation, to planning and agency, come into play. Some of the main challenges that are encountered here are (1) how to provide systematic and ontologically sound bridges from sensory data to affordances and abstract ontological analysis, (2) how to reason logically with common-sense abstractions derived from interactions in a simple robotic world, and (3)  to validate the fruitfulness of enhancing the knowledge layer for the robot’s learning capabilities in a detailed experimental setup.