Sankalp Arora

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About

Sankalp Arora is a leading researcher in autonomous robotics, specializing in informative path planning, safe navigation, and sensor planning. His most-cited work, "Randomized algorithm for informative path planning with budget constraints" (2017, 35 citations), addresses the critical challenge of maximizing information gathered by robots under cost or time constraints—a problem central to exploration and data collection missions. Arora also made significant contributions to safety assurance with "Emergency maneuver library - ensuring safe navigation in partially known environments" (2015, 23 citations), which develops a library of precomputed maneuvers to guarantee safe operation in unstructured, unpredictable settings. His research on sensor planning, notably "PASP: Policy based approach for sensor planning" (2015, 7 citations), enhances robot perception by actively controlling sensor configurations to minimize uncertainty. More recently, Arora has explored data-driven methods in "Data-driven planning via imitation learning" (2018, 5 citations), leveraging learning from demonstrations to optimize task-specific objectives like collision-free navigation and area mapping. With over 70 total citations, his work bridges theoretical optimization and practical deployment, advancing the reliability and efficiency of autonomous systems in real-world environments.