Trust in Language Grounding: a new AI challenge for human-robot teams
This addresses the challenge of effective adoption of language grounding technologies in human-robot teams, but it is incremental as it builds on existing research with a focus on trust.
The paper tackles the problem of ensuring user trust in language grounding for human-robot teams, presenting a survey that includes an overview of AI technologies, six hypothesized trust factors tested empirically on a cleaning team, and future research directions.
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.