Ankur Kamboj

Motilal Nehru National Institute of Technology, Indian Institute of Technology Kanpur

Papers

Total Citations

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About

Ankur Kamboj’s research lies at the intersection of robotics, control theory, and reinforcement learning, with a focus on developing stable and optimal control strategies for robotic manipulators. His work addresses critical challenges in trajectory tracking and energy-efficient operation, particularly for systems deployed in long-duration or resource-constrained environments. Kamboj’s most cited paper, “Event-Triggered Control for Trajectory Tracking by Robotic Manipulator” (2018, 7 citations), introduces an innovative event-triggered framework that reduces computational and communication overhead while maintaining precise tracking performance—a key contribution for real-time robotic applications. In his subsequent work, “Discrete-Time Lyapunov based Kinematic Control of Robot Manipulator using Actor-Critic Framework” (2020, 2 citations), he pioneers a novel integration of Lyapunov stability theory with actor-critic reinforcement learning, ensuring both stability and optimality in discrete-time kinematic control. This dual emphasis on theoretical rigor and practical efficiency distinguishes his approach, offering a pathway for robots to operate autonomously under limited energy or extended hours. Kamboj’s contributions are particularly notable for bridging classical control guarantees with modern learning-based methods, making his research impactful for students and engineers seeking robust, adaptive robotic systems.