Mohit Sajwan
Papers
2
Total Citations
31
H-Index
2
About
Mohit Sajwan is a researcher at the forefront of human-robot collaboration, with a focused expertise in enhancing the perceptual capabilities of collaborative robots (cobots). His work primarily addresses the critical challenge of enabling cobots to safely and efficiently interact with their environment and human coworkers. Sajwan’s major contribution lies in developing efficient surface detection algorithms that allow robots to perceive and navigate dynamic workspaces in real-time, a foundational step for intuitive and safe physical human-robot interaction. His 2022 paper on this topic has garnered 20 citations, reflecting its practical significance in the field. Complementing this technical work, his comprehensive 2023 review paper (11 citations) systematically evaluates the effectiveness of machine learning and deep learning algorithms for cobots, providing a valuable roadmap for future research. Through this dual approach of algorithmic innovation and critical synthesis, Sajwan is helping to bridge the gap between theoretical AI and practical robotic applications, making collaborative robots more adaptable and responsive in industrial and service settings.
Research Focus
Key Achievements
Top Papers
- 1Efficient surface detection for assisting Collaborative Robots20 citations · 2022
- 2