Uncertainty, Vagueness, and Ambiguity in Human-Robot Interaction: Why Conceptualization Matters
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Abstract (arXiv)
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.
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References · 14
- 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,348
- 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 819
- 2023Robots That Ask For Help: Uncertainty Alignment for Large Language Model Plannerscited by 308
- 2023Robot Learning in the Era of Foundation Models: A Surveycited by 38
- 2020When Humans Aren't Optimal: Robots that Collaborate with Risk-Aware Humanscited by 34
- 2019Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interactioncited by 11
- 2024The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learningcited by 11
- 2022Active Uncertainty Reduction for Human-Robot Interaction: An Implicit Dual Control Approachcited by 10
- 2025Robotic Task Ambiguity Resolution via Natural Language Interactioncited by 8
- 2021Talk-to-Resolve: Combining scene understanding and spatial dialogue to resolve granular task ambiguity for a collocated robotcited by 7
- 2024Details Make a Difference: Object State-Sensitive Neurorobotic Task Planningcited by 7
- 2024Advancing Household Robotics: Deep Interactive Reinforcement Learning for Efficient Training and Enhanced Performancecited by 4
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