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About

Yashraj Narang is a robotics researcher whose work spans soft robotics, dexterous manipulation, and sim-to-real transfer, establishing him as a versatile contributor to both physical robot design and intelligent robot learning. His early research pioneered laminar jamming as a transformative mechanism for soft machines, demonstrating how variable-impedance structures could bridge the gap between compliant and rigid robotic systems — work that has accumulated over 370 citations across multiple publications and reshaped how engineers think about tunable-stiffness composites. He further advanced soft robotics through hydrogel-based large-strain sensors, enabling robots to sense deformation without sacrificing compliance. Narang's research evolved toward perception and manipulation, most notably with DexYCB (250 citations), a widely adopted benchmark for hand-object grasping that has become a cornerstone reference for 6D pose estimation and keypoint detection. His subsequent work on DeXtreme, Factory, and IndustReal addresses one of robotics' hardest open problems — transferring contact-rich, dexterous skills from simulation to the real world — combining deep reinforcement learning with careful sim-to-real methodology. Across both hardware innovation and learned robot intelligence, Narang's contributions reflect a rare breadth that makes his work essential reading for anyone serious about real-world robotic manipulation.