Niraj Rathod

Papers

Total Citations

H-Index

About

Niraj Rathod is a robotics researcher specializing in motion planning and control for legged robots, with a particular focus on Model Predictive Control (MPC) applied to dynamic locomotion. His work addresses one of the field's most challenging problems: enabling legged robots to move reliably across unstructured and unpredictable terrain in real time. Rathod's most influential contribution, "Model Predictive Control With Environment Adaptation for Legged Locomotion" (2021, 46 citations), introduced a Nonlinear MPC framework that allows legged robots to continuously replan their motion, adapting to terrain variations and rejecting external disturbances while tracking desired velocities. This work demonstrated strong experimental validation, underscoring its practical relevance. Building on this foundation, his 2021 paper on mobility-enhanced MPC further extended these capabilities to rough terrain scenarios, while his 2023 work tackled a persistent challenge in MPC design — the reliance on heuristic reference signals — by proposing an optimization-based reference generator that improves both feasibility and parameter tuning. Collectively, Rathod's research advances the autonomy and robustness of legged robotic systems, making him a notable contributor to the growing field of real-time optimal control for next-generation mobile robots.