From Interactive to Co-Constructive Task Learning

TL;DR

This paper explores co-construction in human-robot interactive learning, emphasizing joint understanding and execution, and proposes focusing on this process to improve robots' ability to learn from non-expert users in everyday contexts.

Contribution

It introduces the concept of co-construction in robot learning, analyzing its role in human-robot interaction and outlining research directions across architecture, representation, interaction, and explainability.

Findings

Co-construction enables joint task understanding in human-robot interaction.

Insights from adult-child interactions inform robot learning strategies.

Identifies key research areas for advancing co-constructive robot learning.

Abstract

Humans have developed the capability to teach relevant aspects of new or adapted tasks to a social peer with very few task demonstrations by making use of scaffolding strategies that leverage prior knowledge and importantly prior joint experience to yield a joint understanding and a joint execution of the required steps to solve the task. This process has been discovered and analyzed in parent-infant interaction and constitutes a ``co-construction'' as it allows both, the teacher and the learner, to jointly contribute to the task. We propose to focus research in robot interactive learning on this co-construction process to enable robots to learn from non-expert users in everyday situations. In the following, we will review current proposals for interactive task learning and discuss their main contributions with respect to the entailing interaction. We then discuss our notion of co-construction and summarize research insights from adult-child and human-robot interactions to elucidate its nature in more detail. From this overview we finally derive research desiderata that entail the dimensions architecture, representation, interaction and explainability.