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Sunil Kumar Singh is a robotics and control systems researcher whose work spans optimal trajectory generation, adaptive control, and surgical robotics. His most influential contribution, "Optimal Trajectory Generation for Robotic Manipulators Using Dynamic Programming" (1987), established a two-stage framework for off-line trajectory planning and on-line tracking that has garnered 136 citations, making it a foundational reference in robotic motion planning. Building on this, his 1991 work on trajectory planning from a control systems perspective further solidified his expertise in bridging theoretical control principles with practical robotic implementation. Singh's research expanded into adaptive and decentralized control, addressing fundamental challenges in force and impedance behavior for robotic mechanisms. His 1995 analysis of adaptive force control strategies, with 103 citations, demonstrated rigorous theoretical and experimental treatment of a longstanding problem in robotics, particularly removing the need for precise environmental stiffness identification. He also explored robust adaptive controllers for interconnected systems and Lyapunov-based decentralized designs. Notably, Singh contributed to the emerging field of surgical robotics with his 1999 EndoBots system for collaborative control in minimally invasive surgery, reflecting his commitment to translating foundational robotics research into life-improving medical applications. His cumulative body of work remains a valuable resource for students and researchers in intelligent robotic systems.