# A Taxonomy of Conceptual Alignment in Human-Robot Dialogue

arXiv: https://arxiv.org/abs/2606.22360 · HTML: https://mlsanity.com/paper/2606.22360/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 21 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [Learning Perceptual Concepts by Bootstrapping from Human Queries](https://mlsanity.com/paper/2111.05251/) (2021)
- [Grounding Language in Multi-Perspective Referential Communication](https://mlsanity.com/paper/2410.03959/) (2024)
- [Aligning Robot and Human Representations](https://mlsanity.com/paper/2302.01928/) (2023)
- [Concept Alignment](https://mlsanity.com/paper/2401.08672/) (2024)
- [Patterns for Representing Knowledge Graphs to Communicate Situational Knowledge of Service Robots](https://mlsanity.com/paper/2101.10602/) (2021)
- [Communicative Robot Signals: Presenting a New Typology for Human-Robot Interaction](https://mlsanity.com/paper/2302.03971/) (2023)
- [Interactive Acquisition of Fine-grained Visual Concepts by Exploiting Semantics of Generic Characterizations in Discourse](https://mlsanity.com/paper/2305.03461/) (2023)

## Abstract (arXiv)

Successful conversations require speakers to align on the meaning of concepts, a challenging but crucial task for human-robot interaction. Understanding the process of establishing such alignment is hindered by competing interpretations of the term and isolated, unidirectional investigations of its design space. This paper argues for a design-centric understanding of conceptual alignment as a bidirectional and co-constructive process. We introduce a taxonomy that characterizes conceptual alignment dialogues along what triggers its initiation and what level(s) of conceptual understanding it concerns. We further present a dialogue act schema as an operational tool that captures the interactional moves through which alignment is achieved. Together, these contributions provide a structured foundation for analyzing, comparing, and designing conceptual alignment in human-robot interaction.