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.
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.
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.
Effective human-robot collaboration in open-world environments requires joint planning under uncertainty about the task, the environment, and the human teammate. Communication is the most direct means of resolving such uncertainty, yet most existing systems support only one-way communication: robots listen and act, treating humans as passive supervisors rather than conversational teammates capable of two-way dialogue. We propose a unified human-robot joint planning system in which the robot actively resolves uncertainty through two complementary communication channels. When uncertainty is decision-critical, an uncertainty-mitigation joint planning module engages the human in clarification dialogue: it grounds ambiguous instructions via an LLM-assisted active elicitation mechanism, enumerates traversability hypotheses through a hypothesis-augmented A* search, and computes a cost-optimal querying policy via dynamic programming, so that the robot asks only the questions whose answers actually matter for the plan. When explicit dialogue is unnecessary or impractical, a real-time intent-aware collaboration module instead reads implicit, nonverbal cues, maintaining a probabilistic belief over the human's latent task intent from spatial and directional signals to enable coordination-aware task selection without any communication overhead. We validate the proposed system in both Gazebo simulations and real-world UAV deployments, integrated with a voice dialogue interface and a Vision-Language Model (VLM)-based 3D semantic perception pipeline. Experimental results show that cost-optimal clarification dialogue cuts the interaction cost by 51.9% while maintaining a 100% task success rate, and implicit intent reading reduces the cooperative task execution time by 25.4% compared to the baselines.
Zeyu Fang, Yuxin Lin, Cheng Liu +6
Department of Electrical and Computer Engineering, George Washington University · the Department of Electrical and Computer Engineering, Northeastern University · the Department of Mechanical and Aerospace Engineering, George Washington University
Robots in shared workspaces must interpret human actions from partial, ambiguous observations, where overconfident early predictions can lead to unsafe or disruptive interaction. This challenge is amplified in egocentric views, where viewpoint changes and occlusions increase perceptual noise and ambiguity. As a result, downstream human-robot interaction modules require not only an action hypothesis but also a trustworthy estimate of confidence under partial observation. Recent vision-language model-based approaches have been proposed for short-term action recognition due to their open-vocabulary and context-aware reasoning, but their uncertainty reliability in the temporal-prefix regime is largely uncharacterized. We present the first systematic evaluation of uncertainty in vision-language model-based short-term action recognition for human-robot interaction. We introduce a temporal-prefix evaluation protocol and metrics for calibration and selective prediction. We also characterize miscalibration patterns and failure modes under partial observations. Our study provides the missing reliability evidence needed to use vision-language model predictions in confidence-gated human-robot interaction modules.
Zhaoda Du, Michael Bowman, Qiaojie Zheng +1
Colorado School of Mines, Golden, CO 80401, USA · Postdoctoral Fellow in the Cancer Biology Department at the University of Pennsylvania, Philadelphia, PA 19104, USA