Niteesh Sood

Papers

Total Citations

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About

Niteesh Sood is a researcher whose work lies at the intersection of robotics, artificial intelligence, and decision-making under uncertainty. His primary research focuses on developing algorithms for path planning and replanning in environments where information is incomplete or latent, often modeled using Gaussian Processes. His most-cited paper, "No-regret replanning under uncertainty" (2017, 14 citations), addresses the critical challenge of online receding horizon planners operating in latent environments—a problem central to autonomous navigation and adaptive robotics. Sood’s contributions provide a theoretical framework for minimizing regret when replanning paths, ensuring robust performance even as new information emerges. This work has implications for real-world systems like autonomous vehicles and drones, where uncertainty is inherent. While his citation count reflects a growing niche, Sood’s research is notable for its rigorous approach to balancing exploration and exploitation in dynamic settings, offering practical solutions for planners that must adapt on the fly. His achievements demonstrate a commitment to advancing the reliability of autonomous systems, making his work a valuable reference for students and researchers tackling complex, uncertain environments.