Nithyananda B. Kumbla
University of Maryland, College Park, University of Southern California
Papers
6
Total Citations
90
H-Index
6
About
Nithyananda B. Kumbla is a leading researcher in robotic manipulation, with a primary focus on addressing the critical challenges of perception uncertainty in industrial automation. His work centers on developing robust solutions for robotic bin-picking, fixtureless assembly, and human-robot collaboration, where sensor inaccuracies can lead to costly system failures. Kumbla’s most significant contribution is his pioneering framework for simulation-based singulation planning, which evaluates and selects grasp plans that are resilient to pose estimation errors—a problem that plagues real-world bin-picking operations. His 2016 paper on this topic has garnered 40 citations, establishing a foundational approach in the field. He further advanced the domain by introducing methods for remote human intervention in challenging perception scenarios, enabling robots to request assistance when automated systems fail. Kumbla has also made notable strides in fixtureless assembly, developing contact-based probing techniques that reduce part pose uncertainty in collaborative workcells. His research, which consistently addresses the gap between simulation and reality, has been published in top venues and cited over 90 times, marking him as a key innovator in making robotic manipulation more reliable and autonomous in unstructured environments.
Research Focus
Key Achievements
Top Papers
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