About Me
I am currently a Ph.D. candidate at Missouri University of Science and Technology (MST) in the Department of Electrical & Computer Engineering, under the guidance of Dr. Sarangapani.
My research focuses on reinforcement learning-based optimal tracking control for nonlinear discrete-time systems, with applications in robotics and autonomous vehicles. A significant aspect of my work is lifelong learning-based optimal controllers, where the controller continuously learns from past experiences to enhance future performance. I also emphasize safety-aware and explainable AI, ensuring the reliability and interpretability of autonomous decision-making systems.
Beyond my core research, I explore machine learning applications in cyber-physical systems, including:
- Vision-based robotic manipulation and localization
- Motion planning & perception
- SLAM and mapping for real-world autonomous navigation
Research Interests
- Reinforcement Learning & Optimal Control: Developing safe and explainable deep reinforcement learning-based controllers for nonlinear, discrete-time systems, with real-world applications in robotics and autonomous systems.
- Navigation & Motion Planning: Designing adaptive and robust path optimization strategies for autonomous vehicles and mobile robots, enabling efficient navigation in off-road terrains (forests, deserts) and human-centered environments (sidewalks, crowded buildings).
- Perception & Sensor Fusion: Implementing multi-sensor fusion techniques (LiDAR, GPS, IMU) to enhance state estimation, localization, and tracking in dynamic environments.
- Artificial Intelligence in Autonomous Systems: Leveraging deep learning and AI-driven models to enhance decision-making and control in robotics, self-driving cars, and unmanned systems.
- Machine Learning for Control & Simulation: Integrating deep learning-based controllers with traditional model-based control (MPC, PID, fuzzy logic) for improved robustness in nonlinear and uncertain systems.
- Robotics & Autonomous Vehicles: Advancing motion control, planning, and reinforcement learning for humanoid robots, mobile manipulators, and self-driving platforms.
- Safety & Security in Nonlinear Systems: Developing safe reinforcement learning-based controllers with performance guarantees in critical autonomous operations.