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Tyler Errico

dblp:376/9001 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 77% Usability and user experience research · 23%
Artificial intelligence
1 paper
Multi-agent systems · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Multi-agent systems
multi-robot coordination
0.912025
Integrating Human-Robot Teaming Dynamics Into Mission Planning Tools for Transparent Tactics in Multi-Robot Human Integrated Teams · ICRA 2025
Human-robot interaction › multi-robot systems
multi-robot teams
0.912025
Integrating Human-Robot Teaming Dynamics Into Mission Planning Tools for Transparent Tactics in Multi-Robot Human Integrated Teams · ICRA 2025
Usability and user experience research
cognitive load
0.312025
Integrating Human-Robot Teaming Dynamics Into Mission Planning Tools for Transparent Tactics in Multi-Robot Human Integrated Teams · ICRA 2025

Methods — techniques the papers use, named apart from their topics

mission planning tools · 1.7
YearPublicationVenuePosition
2025 Integrating Human-Robot Teaming Dynamics Into Mission Planning Tools for Transparent Tactics in Multi-Robot Human Integrated Teams
abstract
This research aims to demonstrate how integrating human-robot teaming dynamics into mission planning tools impacts the abilities of robot operators as they coordinate multiple robot agents during a mission. This was investigated in a pilot study using two inter-robot collaboration modalities and interface tools, which required different human-robot interaction techniques to execute a mission with a team of four robots. In the first modality, the operator manually inserted waypoints for each robot, as they acted as individual agents. In the second modality, the operator used the Planning Execution to After-Action Review (PETAAR) toolset to plot a single waypoint for the team of robots, as the robots coordinated their movement as a group. One novel component of this study is the investigation of how human-robot teaming dynamics and the PETAAR toolset impacted robot operators' real-time situation awareness and perceived cognitive load as well as team performance. Although the teaming modalities differed greatly with respect to the level of operator input needed, the time required to complete the simulation, the participant's perceived cognitive load, and interface usability were very similar for both modalities. In contrast, the results revealed statistically significant differences between the two teaming modalities related to participants' abilities to maintain a wedge formation while remaining situationally aware. Results from this work will be used to guide development of PETAAR along with the design of future studies investigating more complex teaming scenarios and for creating a baseline for comparing future results.
Audrey L. Aldridge, Tyler Errico, Mitchell Morrell, Cindy L. Bethel, John James, Christa M. Chewar, Michael Novitzky
ICRA2