Zhao Han, Daniel Hammer, Kevin Spevak, Mark Higger, Aaron Fanganello, Neil T Dantam, Tom Williams
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引用次数: 0
Abstract
Introduction: 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.
Method: 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.
Results and discussion: 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.
期刊介绍：
Frontiers in Robotics and AI publishes rigorously peer-reviewed research covering all theory and applications of robotics, technology, and artificial intelligence, from biomedical to space robotics.
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