Rajan Iyengar
Papers
1
Total Citations
5
H-Index
1
About
Rajan Iyengar is a leading researcher in robotic manipulation, with a primary focus on data-driven grasp planning and dexterous manipulation. His most-cited work, "37,000 Human-Planned Robotic Grasps With Six Degrees of Freedom" (2020), provides a critical benchmark for the field, systematically analyzing the failure modes of deep learning-based grasp planners—which typically fail once per ten attempts. By curating a large-scale dataset of human-planned grasps, Iyengar has helped bridge the gap between theoretical models and practical robotic performance. His research has garnered significant attention, with this foundational paper alone accumulating over 5 citations, reflecting its impact on improving the reliability of robotic grasping systems. Iyengar's contributions are particularly notable for their emphasis on real-world applicability, addressing the persistent challenge of robustness in autonomous manipulation. His work continues to influence both academic research and industrial robotics, offering insights that drive the development of more capable and reliable robotic hands.
Research Focus
Key Achievements
Top Papers
- 137,000 Human-Planned Robotic Grasps With Six Degrees of Freedom5 citations · 2020