Victor Sreeram
Papers
3
Total Citations
21
H-Index
3
About
Victor Sreeram is a rising researcher at the forefront of intelligent robotic control, specializing in visual servoing, model predictive control (MPC), and reinforcement learning (RL). His work addresses critical challenges in autonomous manipulation, particularly for constrained and fault-prone robotic systems. Sreeram’s most impactful contribution is his pioneering integration of MPC with RL to tune image-based visual servoing (IBVS) for robot manipulators, a method that elegantly handles nonlinear optimization under physical constraints—a paper that has already garnered 14 citations since 2023. He has further advanced the field by developing fault-tolerant visual servo controllers capable of maintaining performance despite actuator failures, and by combining extreme learning machines with offline RL to enhance adaptive control. His research not only pushes the boundaries of real-time robotic vision and control but also offers practical solutions for industrial automation where reliability and precision are paramount. With a growing citation footprint and a clear trajectory toward robust, learning-driven autonomy, Sreeram is establishing himself as a key voice in the next generation of intelligent robotics.
Research Focus
Key Achievements
Top Papers
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