Abstract concept learning in cognitive robots

DI NUOVO, Alessandro and CANGELOSI, Angelo (2021). Abstract concept learning in cognitive robots. Current Robotics Reports.

[img]
Preview
PDF
DiNuovo-AbstractConceptLearning(VoR).pdf - Published Version
Creative Commons Attribution.

Download (1MB) | Preview
Open Access URL: https://link.springer.com/content/pdf/10.1007/s431... (Published version)
Link to published version:: https://doi.org/10.1007/s43154-020-00038-x
Related URLs:

    Abstract

    Purpose of Review Understanding and manipulating abstract concepts is a fundamental characteristic of human intelligence that is currently missing in artificial agents. Without it, the ability of these robots to interact socially with humans while performing their tasks would be hindered. However, what is needed to empower our robots with such a capability? In this article, we discuss some recent attempts on cognitive robot modeling of these concepts underpinned by some neurophysiological principles. Recent Findings For advanced learning of abstract concepts, an artificial agent needs a (robotic) body, because abstract and concrete concepts are considered a continuum, and abstract concepts can be learned by linking them to concrete embodied perceptions. Pioneering studies provided valuable information about the simulation of artificial learning and demonstrated the value of the cognitive robotics approach to study aspects of abstract cognition. Summary There are a few successful examples of cognitive models of abstract knowledge based on connectionist and probabilistic modeling techniques. However, the modeling of abstract concept learning in robots is currently limited at narrow tasks. To make further progress, we argue that closer collaboration among multiple disciplines is required to share expertise and co-design future studies. Particularly important is to create and share benchmark datasets of human learning behavior.

    Item Type: Article
    Identification Number: https://doi.org/10.1007/s43154-020-00038-x
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 12 Feb 2021 14:56
    Last Modified: 12 Feb 2021 15:00
    URI: http://shura.shu.ac.uk/id/eprint/28133

    Actions (login required)

    View Item View Item

    Downloads

    Downloads per month over past year

    View more statistics