Casey Davis
Papers
1
Total Citations
12
H-Index
1
About
Casey Davis is a robotics researcher whose work focuses on the intersection of computer vision and robotic manipulation, particularly for objects with rotational symmetry. Their most cited paper, "Grasping surfaces of revolution: Simultaneous pose and shape recovery from two views" (2015, 12 citations), addresses a critical challenge in autonomous grasping: handling unknown, rotationally symmetric objects. Davis developed a method to simultaneously estimate the 3D pose and shape of such objects using only two camera views, enabling robots to compute viable grasp points without pre-existing 3D models. This contribution is especially valuable for real-world applications where robots encounter unfamiliar objects, such as in manufacturing or household environments. While their citation count is modest, the work demonstrates a practical, geometry-driven approach to a persistent problem in robotics. Davis’s research exemplifies how clever algorithmic design can expand the capabilities of robotic systems, making them more adaptable to unstructured settings. Their focus on surface-of-revolution objects fills a specific but important niche in the broader field of robotic perception and manipulation.
Research Focus
Key Achievements
Top Papers
- 1