Kishore Reddy Pagidi
Papers
1
Total Citations
3
H-Index
1
About
Kishore Reddy Pagidi is a researcher advancing the frontier of robotic manipulation through data-efficient imitation learning. His primary focus lies in enabling robots to acquire complex manipulation skills from minimal human demonstrations, a critical challenge for deploying robots in unstructured, open-ended environments. His most notable contribution is the development of **Interaction Warping**, a novel method for learning SE(3) robotic manipulation policies from a single demonstration. This approach infers 3D object meshes via shape warping, allowing a robot to generalize a demonstrated task—such as pouring or grasping—to novel object geometries and poses without requiring extensive retraining. While his work has garnered early citations (3 for his 2023 paper), its conceptual impact is significant, offering a practical path toward one-shot imitation learning that bypasses the need for large-scale datasets. Pagidi’s research sits at the intersection of computer vision, geometry processing, and robot learning, and his contributions are poised to influence how robots acquire dexterous skills in real-world settings.
Research Focus
Key Achievements
Top Papers
- 1One-shot Imitation Learning via Interaction Warping3 citations · 2023