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About

Kartik Paigwar is a robotics researcher specializing in legged locomotion, reinforcement learning, and autonomous navigation for mobile robotic systems. His most celebrated contribution is the development of an end-to-end learned locomotion controller for the MIT Mini Cheetah, presented in "Rapid Locomotion via Reinforcement Learning" (2022), which has accumulated over 220 combined citations across its publications. This work achieved record-breaking agility for quadrupedal robots, enabling sustained sprinting at 3.9 m/s and high-speed turning across challenging natural terrains — a landmark result in robot learning research. Beyond high-speed locomotion, Paigwar has made meaningful contributions to accessible robotics. His work on the Stoch 2 quadruped demonstrated that linear policies and augmented random search could produce robust, deployable gaits on sloped terrains and diverse maneuvers, addressing the practical challenge of low-cost hardware deployment. His 2019 deep learning approach to stair detection (38 citations) further showcases his breadth, bridging computer vision and autonomous navigation for search-and-rescue applications. Collectively, Paigwar's research reflects a commitment to making agile, intelligent robot locomotion both state-of-the-art and practically deployable across real-world environments.