Keshav Rajasekaran
Papers
2
Total Citations
15
H-Index
2
About
Keshav Rajasekaran is a researcher at the intersection of robotics, control theory, and computer vision, with a focus on enabling precise manipulation and modeling in complex, stochastic environments. His work addresses fundamental challenges in autonomous systems, particularly where perception and dynamics are uncertain. Rajasekaran’s most notable contribution is a novel visual perception method for low-contrast bright field microscopy, designed to track and estimate the states of microscale objects in heterogeneous microenvironments. This work, which has garnered 10 citations, is critical for advancing automated optical tweezers-based robotic manipulation—a key technology in biophysics and lab-on-a-chip applications. He further extends his impact into control theory with a hierarchical Bayesian linear regression model that incorporates local features to approximate stochastic dynamics. This model, cited 5 times, offers a powerful framework for state prediction in autonomous systems where traditional models fall short. By bridging perception and dynamics, Rajasekaran’s research provides essential tools for building more robust, adaptive robots capable of operating in the messy, unpredictable conditions of the real world.
Research Focus
Key Achievements
Top Papers
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