Luke Barbier

I’m a robotics and ML engineer with a particular interest in JEPA-style world models for physical interaction. I’m interested in building systems that learn predictive latent representations and use them for hierarchical planning, adaptive computation, flexible goal specification, and robust decision-making under partial observability and changing camera viewpoints. More broadly, I care about how learned world models can become reliable components of real robotic systems rather than isolated perception models.

Portrait of Luke Barbier

Publications

Lifting Motion to the 3D World

Distributed Event-Triggered Localization for High Latency Communication

Barbier, Luke, Luke Morrissey, Nisar Ahmed, Eric Frew, Sonia Martinez, and Kenneth Center

American Controls Conference (ACC) 2022

Lifting Motion to the 3D World

Collaborative human-autonomy semantic sensing through structured POMDP planning

Burks, Luke, Nisar Ahmed, Ian Loefgren, Barbier, Luke, Jeremy Muesing, Jamison McGinley, and Sousheel Vunnam

Robotics and Autonomous Systems 2021

Lifting Motion to the 3D World

Closed-Loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search

Burks, Luke, Ian Loefgren, Barbier, Luke, Jeremy Muesing, Jamison McGinley, Sousheel Vunnam, and Nisar Ahmed

International Conference on Information Fusion (FUSION) 2018