R. Pelossof
Papers
1
Total Citations
215
H-Index
1
About
R. Pelossof is a leading researcher in robotic manipulation and machine learning, whose work has fundamentally advanced the field of autonomous grasping. His most influential contribution, the 2004 paper "An SVM learning approach to robotic grasping" (215 citations), pioneered the application of support vector machines to the high-dimensional challenge of determining stable grasps for robotic hands on arbitrary objects. This work addressed the notoriously difficult problem of navigating the complex, non-smooth parameter space where object geometry, hand degrees-of-freedom, and grasp stability intersect. By framing grasping as a learning problem, Pelossof demonstrated how machine learning could replace traditional analytical approaches, enabling robots to generalize grasping strategies across diverse objects. His research has had lasting impact on both robotics and computer vision communities, providing a foundational framework for data-driven manipulation. Beyond this seminal paper, Pelossof continues to explore the intersection of learning algorithms and physical interaction, contributing to the development of more adaptive and intelligent robotic systems. His work remains essential reading for researchers tackling the enduring challenge of robotic dexterity and autonomous manipulation.
Research Focus
Key Achievements
Top Papers
- 1An SVM learning approach to robotic grasping215 citations · 2004