Suraj Kothawade

Papers

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About

Suraj Kothawade’s research bridges robotics, computer vision, and human-robot collaboration, with a focus on enabling machines to operate effectively in complex, unstructured environments. His most cited work, “Robotic Lime Picking by Considering Leaves as Permeable Obstacles” (2021, 13 citations), addresses a critical challenge in agricultural robotics: dense foliage that obstructs fruit grasping. Rather than treating leaves as hard obstacles to be avoided—a common but inefficient approach—Kothawade innovatively models them as permeable, allowing robotic arms to push through foliage while minimizing damage. This practical insight improves harvesting efficiency and has implications for other occluded manipulation tasks. In “Learning Collaborative Action Plans from YouTube Videos” (2022, 5 citations), he explores how robots can learn cooperative behaviors by observing human demonstrations from unconstrained video sources, reducing the need for costly manual programming. His work demonstrates a talent for extracting robust, transferable knowledge from noisy real-world data. Kothawade’s contributions are particularly notable for their direct applicability to agriculture and service robotics, where adaptability and safety are paramount. His research offers a compelling model for students interested in deploying intelligent systems in the wild.