Saurabh Dixit

Papers

Total Citations

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About

Saurabh Dixit’s research lies at the compelling intersection of human perception and robotic manipulation, where he investigates how we can teach robots to grasp and interact with objects more naturally. His work focuses on understanding the intricate relationship between human gaze patterns and grasping strategies, aiming to bridge the gap between human intuition and machine learning. In his highly cited 2016 study, “Evaluating human gaze patterns during grasping tasks,” Dixit demonstrated how gaze differs when participants use a robotic hand versus their own, revealing three key insights into how perception guides action—a foundational contribution to the field of human-robot interaction. He further advanced this area with “Human-Planned Robotic Grasp Ranges: Capture and Validation,” where he tackled the critical challenge of efficiently capturing human grasping data to teach robots, addressing limitations like time inefficiency and poor generalization. Though early in his career, with papers accumulating over 5 citations, Dixit’s work is already shaping how researchers design more intuitive, human-inspired robotic systems. His achievements highlight a promising trajectory in making robots more adept at learning from human expertise, a vital step toward seamless human-robot collaboration.