AUTHOR=Han Zhao , Hammer Daniel , Spevak Kevin , Higger Mark , Fanganello Aaron , Dantam Neil T. , Williams Tom 

TITLE=Givenness hierarchy theoretic sequencing of robot task instructions

JOURNAL=Frontiers in Robotics and AI

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2025.1640535

DOI=10.3389/frobt.2025.1640535

ISSN=2296-9144

ABSTRACT=IntroductionWhen 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.MethodIn 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.Results and discussionOur 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.