Christine T. Chang — accepted AI-conference papers

2025

Iteratively Adding Latent Human Knowledge Within Trajectory Optimization Specifications Improves Learning and Task Outcomes

Christine T. Chang, Maria P. Stull, Breanne Crockett, Emily Jensen, Clare Lohrmann, Mitchell Hebert +1

RA-L 2025

Frictionless and understandable tasking is essential for leveraging human-autonomy teaming in commercial, military, and public safety applications. Existing technology for facilitating human teaming with uncrewed aerial vehicles (UAVs), utilizing planners or trajectory optimizers that incorporate hu

Cited by 1 Source ↗Scholar ↗

2023

Human Non-Compliance with Robot Spatial Ownership Communicated via Augmented Reality: Implications for Human-Robot Teaming Safety

Christine T. Chang, Matthew B. Luebbers, Mitchell Hebert, Bradley Hayes

ICRA 2023poster

Ensuring the safety and efficiency of human workers in environments shared with autonomous robots is of paramount importance. In this work we examine the behavior and attitudes of participants performing tasks in a noisy environment collocated with an autonomous quadcopter robot. Visual communicatio…

Cited by 5 Source ↗Scholar ↗