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About

Andrea Bajcsy is a roboticist whose research sits at the intersection of human-robot interaction, motion planning, and robot learning, with a particular focus on enabling robots to operate safely and intelligently alongside humans. Her most influential work addresses one of robotics' core challenges: how robots can predict, interpret, and adapt to human behavior in real time. Her 2019 paper on confidence-aware motion prediction (115 citations) introduced a framework allowing robots to reason about uncertainty in human motion models, directly improving collision avoidance. This theme of uncertainty-awareness carries throughout her research, from probabilistically safe planning to robust control frameworks for human motion prediction. Bajcsy has made significant contributions to learning robot objectives from physical human interaction—demonstrating that when a person physically corrects a robot mid-task, that guidance contains rich information about human preferences that robots can extract feature by feature. Her more recent work expands into game-theoretic human modeling, conformal decision theory, and contingency planning for multi-agent systems, reflecting a sophisticated, mathematically grounded approach to robot decision-making. With over 370 total citations across her top papers and contributions spanning safety guarantees, interactive learning, and strategic planning, Bajcsy has established herself as a leading voice in building robots that are not merely functional, but genuinely trustworthy partners for humans.