Nikhil Ranganathan

Cornell University, California Institute of Technology

Papers

Total Citations

334

H-Index

About

Nikhil Ranganathan is a pioneering researcher at the intersection of robotics, computer vision, and statistical mechanics. His work spans two distinct but equally impactful domains: collective robotic systems and thermal perception for autonomous navigation. Ranganathan’s most influential contribution is the development of “particle robotics,” a paradigm inspired by statistical mechanics of loosely coupled components. His 2019 paper on this topic, which has garnered 321 citations, demonstrates how simple, non-communicating units can achieve complex, coordinated tasks through stochastic interactions—a breakthrough with profound implications for swarm robotics and modular systems. More recently, Ranganathan has advanced autonomous perception in extreme environments. He co-authored the Caltech Aerial RGB-Thermal Dataset in the Wild (2024), a benchmark enabling robust vision in low-light and adverse weather. His latest work, MonoTher-Depth (2025), introduces confidence-aware distillation to enhance monocular depth estimation from thermal images, addressing critical data scarcity challenges. By bridging foundational physics-inspired robotics with practical thermal vision, Ranganathan’s research empowers robots to operate reliably where conventional sensors fail, making him a key figure in resilient autonomous systems.