Title
Development Of A Squad Level Vocabulary For Human-Robot Interaction
Keywords
Human-robot interaction; human-robot teaming; mixed-initiative teams; speech recognition
Abstract
Interaction with robots in military applications is trending away from teleoperation and towards collaboration. Enabling this transition requires technologies for natural and intuitive communication between Soldiers and robots. Automated Speech Recognition (ASR) systems designed using a well-defined lexicon are likely to be more robust to the challenges of dynamic and noisy environments inherent to military operations. To successfully apply this approach to ASR development, lexicons should involve an early focus on the target audience. To facilitate development a vocabulary focused at the squad level for Human Robot Interaction (HRI), 31 Soldiers from Officer Candidate School at Ft. Benning, GA provided hypothetical commands for directing an autonomous robot to perform a variety of spatial navigation and reconnaissance tasks. These commands were analyzed, using word frequency counts and heuristics, to determine the structure and word choice of commands. Results presented provide a baseline Squad Level Vocabulary (SLV) and a foundation for development of HRI technologies enabling multi-modal communications within mixed-initiative teams. © 2014 Springer International Publishing.
Publication Date
1-1-2014
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
8525 LNCS
Issue
PART 1
Number of Pages
139-148
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1007/978-3-319-07458-0_14
Copyright Status
Unknown
Socpus ID
84903627076 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84903627076
STARS Citation
Barber, Daniel; Wohleber, Ryan W.; Parchment, Avonie; Jentsch, Florian; and Elliott, Linda, "Development Of A Squad Level Vocabulary For Human-Robot Interaction" (2014). Scopus Export 2010-2014. 9292.
https://stars.library.ucf.edu/scopus2010/9292