Rangoli Sharan

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About

Rangoli Sharan is a researcher specializing in autonomous systems, robotic manipulation, and formal methods for decision-making under uncertainty. Her work sits at the intersection of artificial intelligence, control theory, and robotics, addressing some of the field's most challenging problems: enabling robots to operate reliably in uncertain, dynamic, and human-proximate environments. Her most cited contribution, "Model-based autonomous system for performing dexterous, human-level manipulation tasks" (2013, 18 citations), advances the frontier of robotic dexterity, pushing machines closer to human-like physical capability. Building on this, her 2014 work on formal methods for control synthesis in partially observed environments (14 citations) tackles the critical challenge of making robots adaptable on-the-fly — a prerequisite for real-world deployment at scale. More recently, her research on stochastic finite state control of POMDPs with Linear Temporal Logic specifications (2020, 12 citations) addresses the notoriously intractable problem of optimal decision-making under uncertainty, with direct applications to robot manipulation and autonomous vehicles. Across her body of work, Sharan consistently bridges rigorous theoretical frameworks with practical robotic applications, making her research highly relevant to both academic and industry audiences working on the next generation of intelligent autonomous systems.