Yash Shahapurkar
Papers
2
Total Citations
5
H-Index
2
About
Yash Shahapurkar’s research lies at the intersection of robotic manipulation, industrial automation, and intelligent control systems, with a focus on making robots more adaptive in uncertain, real-world environments. His work addresses fundamental challenges in manufacturing and logistics, particularly in assembly and grasping operations. In his 2018 paper on integrating impedance control with learning-based search schemes, Shahapurkar tackled the problem of robotic assembly under uncertainty—a critical issue for high-volume production where traditional fixed-fixture methods are economically impractical. This work, which has garnered 3 citations, proposes a hybrid approach that combines compliant control with intelligent search to enable robots to handle part variability and misalignment. His 2020 study on industrial robot grasping using deep learning and programmable logic controllers (PLCs) addresses the grand challenge of universal grasping for e-commerce and manufacturing. With 2 citations, this work demonstrates how deep learning-based grasping can be deployed on industrial hardware, bridging the gap between cutting-edge AI and practical factory-floor implementation. Shahapurkar’s contributions are particularly notable for their focus on making advanced robotics accessible and cost-effective for industry, offering pathways to more flexible, autonomous production systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2