Devesh Upadhyay
Papers
1
Total Citations
7
H-Index
1
About
Devesh Upadhyay is a rising researcher in robotics and autonomous systems, with a primary focus on motion planning and control for robotic manipulators in complex, crowded environments. His most notable contribution is the development of an efficient motion planning framework that integrates Control Barrier Functions (CBFs) with neural controllers, addressing the critical challenge of real-time collision avoidance. This work, published in 2024 and already garnering 7 citations, proposes a generalizable CBF-based steering method that dramatically reduces the computational burden of traditional sampling-based planners by replacing expensive collision checks with learned neural policies. Upadhyay’s approach enables manipulators to navigate tight spaces safely and swiftly, bridging the gap between theoretical safety guarantees and practical deployment. His research is particularly impactful for applications in collaborative robotics, warehouse automation, and human-robot interaction, where speed and safety are paramount. By combining rigorous control theory with modern machine learning, Upadhyay is helping to make autonomous manipulation more reliable and accessible. As an early-career scholar, his work signals a promising trajectory toward scalable, real-time motion planning solutions.
Research Focus
Key Achievements
Top Papers
- 1