ORIGINAL RESEARCH article
Front. Robot. AI
Sec. Human-Robot Interaction
Volume 12 - 2025 | doi: 10.3389/frobt.2025.1640535
Givenness Hierarchy Theoretic Sequencing of Robot Task Instructions
Provisionally accepted
- 1University of South Florida, Tampa, United States
- 2Colorado School of Mines, Golden, United States
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When collaborative robots teach human teammates new tasks, they must carefully determine the order to explain different parts of the task. In robotics, this problem is especially challenging, due to the situated and dynamic nature of robot task instruction. In this work, we consider how robots can leverage the Givenness Hierarchy to "think ahead" about the objects they must refer to so that they can sequence object references to form a coherent, easy-to-follow series of instructions. Our experimental results (n=82) show that robots using this GH-informed planner generate instructions that are more natural, fluent, understandable, and intelligent, less workload demanding, and that can be more efficiently completed.
Keywords: Givenness Hierarchy, document planner, natural-language generation, anaphora generation, Collaborative robotics
Received: 03 Jun 2025; Accepted: 20 Aug 2025.
Copyright: © 2025 Han, Hammer, Spevak, Higger, Fanganello, Dantam and Williams. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Tom Williams, Colorado School of Mines, Golden, United States
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