Givenness hierarchy theoretic sequencing of robot task 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.
Authors
- Neil T. Dantam (ORCID: https://orcid.org/0000-0002-0907-2241)
- Kevin Spevak
- Daniel Hammer
- Aaron Fanganello (ORCID: https://orcid.org/0009-0008-5917-9961)
- Mark Higger (ORCID: https://orcid.org/0009-0007-1263-1204)
- Tom Williams
- Zhao Han
Institutions
- Colorado School of Mines (US)
Publication Details
- Journal
- Frontiers in Robotics and AI
- Published
- 2025-09-24
- DOI
- https://doi.org/10.3389/frobt.2025.1640535
- Citations
- 1
- Primary Topic
- Robot Manipulation and Learning
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- Office of Naval Research