Marius Zoellner
Papers
1
Total Citations
3
H-Index
1
About
Marius Zoellner’s research focuses on robotic manipulation, 3D perception, and object representation, with a particular emphasis on enabling robots to interact intelligently with their environments. His most cited work, “Using superquadrics for the approximation of objects and its application to grasping” (2010), introduces a novel approach to abstracting raw point cloud data from 3D laser scanners into high-level superquadric models. This representation simplifies complex object geometries, allowing robots to plan and execute grasps more efficiently—a critical capability for household and industrial automation. By bridging the gap between sensor data and actionable robotic tasks, Zoellner’s contributions address fundamental challenges in autonomous manipulation. While his citation count (3) reflects the niche, foundational nature of his work, it has informed subsequent advances in object approximation and grasp planning. His research underscores the importance of geometric abstraction in robotics, offering a pathway toward more adaptable and dexterous robotic systems. For students and researchers, Zoellner’s work exemplifies how elegant mathematical models can transform raw perception into practical robotic action.
Research Focus
Key Achievements
Top Papers
- 1