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About

Gokul Swamy is a researcher working at the intersection of robotics, human-robot interaction (HRI), and machine learning, with a particular focus on how intelligent systems can learn safely and efficiently alongside humans. His most influential work, "On the Utility of Model Learning in HRI" (2019, 42 citations), addresses one of robotics' foundational tensions: whether robots should build explicit models of their environment — including human behavior — or learn policies directly through experience. This contribution has become a key reference point for researchers designing adaptive robotic systems that interact with people. Swamy's work spans several compelling directions. His research on shared safety constraints (2023) explores how agents can extract generalizable safety rules from demonstrations across multiple tasks, a critical step toward deploying robots in real-world environments like kitchens or warehouses. His "Scaled Autonomy" work (2020) tackles the practical challenge of human operators supervising fleets of semi-autonomous robots, blending human oversight with machine efficiency. Most recently, his 2025 work on vision-language models for policy steering signals a forward-looking interest in grounding robot decision-making in richer semantic understanding. Together, these contributions reflect a research agenda deeply committed to making autonomous systems both capable and trustworthy.