Uncertainty, Vagueness, and Ambiguity in Human-Robot Interaction: Why Conceptualization Matters

Xiaowen Sun; Cornelius Weber; Matthias Kerzel; Josua Spisak; Stefan Wermter

cs.HCMar 10, 2026

Uncertainty, Vagueness, and Ambiguity in Human-Robot Interaction: Why Conceptualization Matters

Authors: Xiaowen Sun, Cornelius Weber, Matthias Kerzel, Josua Spisak, Stefan Wermter

Organizations: The Authors are with the Knowledge Technology Group, Department of Informatics, University of Hamburg, 22527 Hamburg, Germany.

Abstract

Uncertainty, vagueness, and ambiguity are closely related and often confused concepts in human-robot interaction (HRI). In earlier studies, these concepts have been defined in contradictory ways and described using inconsistent terminology. This conceptual confusion and lack of terminological consistency undermine empirical comparability, thereby slowing the accumulation of theory. Consequently, consistent concepts that clarify these challenges, including their definitions, distinctions, and interrelationships, are needed in HRI. To address this lack of clarity, this paper proposes a consistent conceptual foundation for the challenges of uncertainty, vagueness, and ambiguity in HRI. First, we examine the meanings of these three terms in dictionaries. We then analyze the nature of their distinctions and interrelationships within the context of HRI. We further illustrate these characteristics through examples. Finally, we demonstrate how this consistent conceptual foundation facilitates the design of novel methods and the evaluation of existing methodologies for these phenomena.

Figures & tables

Fig. 1: The relationship of uncertainty, vagueness, and ambiguity in the interdisciplinary research of linguistics and HRI. Uncertainty refers to states of incomplete knowledge. Vagueness pertains to concepts with indeterminate boundaries. Ambiguity arises when a linguistic or symbolic expression maps to two or more distinct interpretations. These three phenomena can occur independently or in combination, as illustrated by the smaller circles.

Type	Instruction	Explanation
Uncertainty	Pick up the toy car from under the table.	Due to the toy car not being in the robot’s view, the robot is uncertain whether it is under the table or not.
Ambiguity	Pick up the apple on the table.	There are multiple apples. The instruction is ambiguous because it could refer to more than one object.
Vagueness	Clean this table thoroughly.	‘Thoroughly’ is not precisely defined; boundaries of the task are unclear, leading to vagueness in this command.
Uncertainty and Ambiguity	Deliver this glass to another room.	There are many rooms; ‘another’ is ambiguous . From the robot’s current location to another room, the environment is dynamic, with people and obstacles moving unpredictably. The robot cannot predict exactly where obstacles will be; there is uncertainty .
Vagueness and Ambiguity	Move closer to the table.	There are multiple tables in the room, and ‘closer’ is not quantified. This command combines ambiguity (which table?) and vagueness (how close?).
Uncertainty and Vagueness	Can you clean this messy area later?	The words, ‘this messy area’ and ‘later’ lack clear boundaries. In a very messy scene, the robot may lack the capability to clean it. Therefore, this command contains vagueness and uncertainty .

TABLE I: This table illustrates UVA-phenomena, distinct yet partially overlapping concepts in everyday life (see Fig. 2 ). Instructions are what a user tells the robot, while explanations provide the background and associated problems.

Fig. 2: An everyday life scenario involving a humanoid intelligent robot. Source: AI-generated via ChatGPT.

Human-Robot Interaction

Human-robot interaction studies how people and robots communicate, coordinate, and safely work or live together.

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Jun 21, 2026J/K move · Enter open · S save

A Taxonomy of Conceptual Alignment in Human-Robot Dialogue

Shengchen Zhang, Xiaohua Sun, Weiwei Guo

College of Design and Innovation, Tongji University, Shanghai, China. · School of Design, Southern University of Science and Technology, Shenzhen, Guangdong, China.

Spoken Dialogue Systems Concept Alignment Human-Robot Interaction

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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.

Abstract

Human-Robot Interaction

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