Alice Yepremyan
Papers
1
Total Citations
3
H-Index
1
About
Dr. Alice Yepremyan is pioneering the application of contrastive learning to onboard vision systems for planetary robotics. Her flagship work, "CLOVER: Contrastive Learning for Onboard Vision-Enabled Robotics" (2023), directly tackles a critical bottleneck in space exploration: the scarcity of annotated training data for planetary images. By introducing a self-supervised framework, Dr. Yepremyan's approach enables robotic rovers and landers to learn robust visual representations without costly human labeling, dramatically improving their ability to recognize terrain, hazards, and scientific targets in real-time. This innovation addresses the pervasive problem of inductive bias, where models trained on data from one spacecraft fail when deployed on another—a challenge that has long hindered autonomous operations across diverse missions. Though early in her career, her work has already garnered attention for its potential to reduce reliance on Earth-based commands, paving the way for more adaptive and resilient exploration of Mars, the Moon, and beyond. Dr. Yepremyan’s research sits at the intersection of computer vision, self-supervised learning, and space robotics, promising to fundamentally reshape how future missions perceive and interact with alien worlds.
Research Focus
Key Achievements
Top Papers
- 1CLOVER: Contrastive Learning for Onboard Vision-Enabled Robotics3 citations · 2023