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About

Gayane Kazhoyan is a robotics researcher whose work sits at the intersection of cognitive architectures, knowledge representation, and autonomous robot planning. Her research focuses on enabling robots to perform flexible, generalizable tasks in real-world environments — a challenge she addresses through multiple complementary approaches. Among her most significant contributions is her work on cloud robotics and knowledge exchange, which proposes crowd-sourcing solutions to the "knowledge gap problem" by allowing robots to share encyclopedic knowledge through cloud infrastructure (40 citations). She has also made notable strides in developing action descriptions as a framework for generalizing robot control programs across diverse objects, tasks, and environments (18 citations), and in designing cognition-enabled frameworks for mixed human-robot rescue teams (18 citations). Her research on self-specializing robot plans based on experience (17 citations) further demonstrates her commitment to bridging the gap between laboratory robotics and real-world deployment. A highlight of her career is her contribution to CRAM, a hybrid cognitive architecture that tackles key challenges in everyday robot manipulation. Kazhoyan's body of work collectively advances the goal of robots that can reason, adapt, and learn autonomously in unstructured environments.