Papers
4
Total Citations
40
H-Index
3
About
Padmaja Kulkarni is a robotics researcher whose work focuses on making robots more adaptable, intuitive, and capable in real-world tasks. Her primary research areas include reinforcement learning for assembly, gesture-based robot control, and sensor-integrated grasping. Kulkarni’s most impactful contribution is her 2021 paper on combining impedance control with residual recurrent reinforcement learning, which enables robots to learn assembly tasks in just a few minutes—a significant leap in efficiency that has garnered 28 citations. This work addresses the critical challenge of adapting to uncertainties in real-world manufacturing. She also pioneered a gesture recognition framework using IMUs and an Online Lazy Neighborhood Graph search, allowing natural human-robot interaction without cumbersome physical controls. In agricultural robotics, Kulkarni developed a geometry-based grasping method for vine tomatoes, demonstrating how computer vision and geometric modeling can improve delicate harvesting. Her additional work on low-cost sensor integration for flexible robotic fingers shows her commitment to practical, accessible solutions. With a citation count approaching 40 across her key papers, Kulkarni is advancing the frontier of robotic dexterity and human-robot collaboration, making her research particularly valuable for students and engineers working on applied robotics and automation.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Geometry-Based Grasping of Vine Tomatoes4 citations · 2021
- 4