Abstract Concept Learning in Cognitive Robots

Alessandro Di Nuovo,Angelo Cangelosi

doi:10.1007/s43154-020-00038-x

Alessandro Di Nuovo, Angelo Cangelosi

Open Access

https://doi.org/10.1007/s43154-020-00038-x

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Abstract

Purpose of ReviewUnderstanding 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 FindingsFor 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.SummaryThere 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.

Highlights

Human intelligence is characterized by the ability to create and manipulate abstract concepts like “wisdom” and “love.” This ability is at the core of human creativity, and, it is required for advanced cognitive capabilities like the retrieval of past thoughts and memories, relational reasoning and problem-solving in current situations, and the processing of thoughts linked to the future
1 Sheffield Robotics, Sheffield Hallam University, Sheffield, UK 2 Department of Computer Science, University of Manchester, Manchester, UK 3 ISTC-CNR, Palermo, Italy human language, where abstract words are often used in daily conversations to represent emotions, events, and situations that occur in physical environments and social interactions among people
Most neuro-psychological contributions recognize that an extension beyond a purely grounded approach is needed to fully account for the representation of abstract concepts

Summary

Introduction

Human intelligence is characterized by the ability to create and manipulate abstract concepts like “wisdom” and “love.” This ability is at the core of human creativity, and, it is required for advanced cognitive capabilities like the retrieval of past thoughts and memories, relational reasoning and problem-solving in current situations, and the processing of thoughts linked to the future (e.g., planning and design). We will present a “direct grounding” strategy for the embodied learning of numerical concepts that combines gestures and action with words, such as in the use of finger counting representations for augment teaching a child (or a robot) about numbers. Cognitive models that enable robots to learn new words and concepts typically adopt an embodied and grounded approach.

Results

Conclusion