Mausam Mausam

University of Washington, Indian Institute of Technology Delhi

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About

Mausam Mausam is a leading researcher in artificial intelligence, with a primary focus on automated planning, reasoning under uncertainty, and robot task planning. His work bridges foundational AI theory with practical robotics, aiming to enable intelligent agents to make robust decisions in complex, real-world environments. A major contribution is his pioneering research on planning in Markov Decision Processes (MDPs) with unknown dynamics, as detailed in his highly cited 2010 paper (23 citations), which addresses the challenge of computing optimal policies when models are initially uncertain. This work has significant implications for domains like space robotics and healthcare. More recently, Mausam has advanced the frontier of robot learning and common-sense reasoning. His work on TOOLTANGO (2022) tackles the critical problem of enabling robots to generalize knowledge about tool use for novel tasks, while GoalNet (2022) focuses on learning from human demonstrations to follow natural language instructions. Through these contributions, Mausam is shaping how robots can acquire and apply common-sense knowledge, moving beyond rigid programming toward flexible, intelligent collaboration.