Brian Wilson

Jet Propulsion Laboratory, California Institute of Technology

Papers

Total Citations

H-Index

About

Brian Wilson’s research bridges the frontiers of autonomous robotics and planetary exploration, with a focus on enabling deep learning for in situ science on extraterrestrial surfaces. His key contributions center on developing self-supervised and contrastive learning methods—such as those in his works “Self-supervised Distillation for Computer Vision Onboard Planetary Robots” (2023, 4 citations) and “CLOVER: Contrastive Learning for Onboard Vision-Enabled Robotics” (2023, 3 citations)—to overcome critical challenges like scarce annotated planetary imagery and domain shifts between spacecraft datasets. These innovations allow robotic agents to perceive and understand their surroundings with minimal human input, maximizing science return while reducing risk on missions beyond Mars. Earlier in his career, Wilson contributed to atmospheric science with “The aerosol measurement and processing system (AMAPS)” (2010, 3 citations), showcasing his versatility. Though his citation counts are modest, his work is foundational for autonomous planetary explorers, directly addressing the bottleneck of data scarcity in extraterrestrial environments. For students and researchers, Wilson’s research exemplifies how creative machine learning solutions can empower robots to operate independently on distant worlds, paving the way for future missions to moons and planets where real-time human control is impossible.