Preston Culbertson’s research seeks to understand robustness in robot learning, with a particular focus on dexterous manipulation and tool use. His work combines ideas from optimization, control theory, and machine learning to study how robots can remain reliable when models, sensors, or hardware are imperfect. The goal is to develop robotic systems that can manage uncertainty, adapt, and improvise when deployed in messy real-world environments.