Shreeyak S. Sajjan

Papers

Total Citations

278

H-Index

About

Shreeyak S. Sajjan is a researcher whose work sits at the critical intersection of computer vision and robotics, with a primary focus on enabling machines to perceive and manipulate objects that have historically been invisible to standard 3D sensors. Sajjan’s most significant contribution is the development of **ClearGrasp**, a deep learning framework for the 3D shape estimation of transparent objects. This work directly tackles a fundamental challenge in robotic manipulation: while everyday items like glassware and plastic bottles are ubiquitous, their optical properties cause them to appear as noisy, distorted approximations of the surfaces behind them in standard depth cameras. The flagship paper on this topic, published in 2020, has garnered **over 258 citations**, underscoring its profound impact on the field. By providing a method to reconstruct accurate geometry from a single RGB-D image, ClearGrasp enables robots to reliably grasp and interact with transparent objects—a capability previously out of reach. This achievement not only advances the state of the art in perception but also has direct, practical implications for automating tasks in homes, warehouses, and laboratories. Sajjan’s work is a landmark in bridging the gap between challenging real-world visual data and robust robotic action.