Trust in Language Grounding: a new AI challenge for human-robot teams

TL;DR

This paper explores the emerging field of trust in language grounding for human-robot teams, reviewing AI techniques, trust factors, and proposing future research directions to enhance adoption.

Contribution

It provides an overview of language grounding research, identifies six trust factors tested empirically, and suggests future research avenues in trust for human-robot interaction.

Findings

Six trust factors empirically tested on a human-robot cleaning team

Identified key AI technologies and data sets for language grounding

Proposed future research directions for trust in language grounding

Abstract

The challenge of language grounding is to fully understand natural language by grounding language in real-world referents. While AI techniques are available, the widespread adoption and effectiveness of such technologies for human-robot teams relies critically on user trust. This survey provides three contributions relating to the newly emerging field of trust in language grounding, including a) an overview of language grounding research in terms of AI technologies, data sets, and user interfaces; b) six hypothesised trust factors relevant to language grounding, which are tested empirically on a human-robot cleaning team; and c) future research directions for trust in language grounding.