Victoria Florence
Papers
2
Total Citations
5
H-Index
2
About
Victoria Florence is a roboticist focused on enabling robots to perceive and interact with unfamiliar objects in unstructured, real-world environments. Her core research lies at the intersection of computer vision and robotic manipulation, specifically advancing self-supervised learning and video object segmentation for autonomous systems. In her 2019 work, Florence pioneered self-supervised methods for in-hand object learning, allowing robots to recognize and segment novel objects without requiring extensive labeled datasets—a critical step toward scalable, general-purpose robotics. She extended this foundation in 2020 by integrating video object segmentation with visual servo control and depth estimation on a mobile robot, demonstrating how dense object-background separation can directly inform real-time manipulation and spatial reasoning. Though early in her career, Florence’s contributions address a fundamental bottleneck in robotics: the ability to handle unseen objects in new settings. Her work bridges the gap between state-of-the-art vision algorithms and practical robotic control, with implications for household assistants, warehouse automation, and field robotics. By prioritizing self-sufficiency over supervised learning, she is helping shape a future where robots can learn on the job.
Research Focus
Key Achievements
Top Papers
- 1Self-Supervised Robot In-hand Object Learning.3 citations · 2019
- 2