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SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
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Givenness hierarchy theoretic sequencing of robot task instructions.

Zhao Han1, Daniel Hammer1, Kevin Spevak1

  • 1Department of Computer Science, Colorado School of Mines, Golden, CO, United States.

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Summary

Robots can improve task instruction by using the Givenness Hierarchy (GH) to plan object references. This makes robot-taught tasks easier for humans to understand and complete efficiently.

Keywords:
anaphora generationcollaborative roboticsdocument planninggivenness hierarchynatural-language generation

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Area of Science:

  • Robotics
  • Human-Robot Interaction
  • Cognitive Science

Background:

  • Collaborative robots teaching humans new tasks requires careful sequencing of instructions.
  • The situated and dynamic nature of robot task instruction presents unique challenges.

Purpose of the Study:

  • To investigate how robots can leverage the Givenness Hierarchy (GH) for planning object references.
  • To generate coherent and easy-to-follow instructions for human teammates.

Main Methods:

  • Utilized the Givenness Hierarchy (GH) as a planning framework.
  • Developed a GH-informed planner for sequencing object references in robot instructions.

Main Results:

  • Robot-generated instructions were rated as more natural, fluent, understandable, and intelligent.
  • Instructions led to reduced workload for human participants.
  • Task completion efficiency was improved.

Conclusions:

  • Leveraging the Givenness Hierarchy (GH) enhances robot instruction generation.
  • GH-informed planning leads to more effective human-robot collaboration in task learning.