Year: 2025
Type: article
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.
Source: Frontiers in Robotics and AI
Institution: Colorado School of Mines
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,353
Funder: Office of Naval Research
Awards: G8876996369, G959850068
OpenAlex ID: W4414486017