Angela Chao

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About

Angela Chao is a rising researcher in robot learning and imitation, whose work tackles the fundamental challenge of translating human demonstrations into executable robot actions. Her most-cited paper, "One-Shot Imitation Under Mismatched Execution" (2025), addresses a critical bottleneck in robotics: the execution mismatches between human movement styles and robot physical capabilities. By developing methods that allow robots to learn from a single human demonstration despite these differences, Chao is pushing the boundaries of few-shot imitation learning for long-horizon manipulation tasks. Her research bridges human-robot interaction and machine learning, with implications for accessible robot programming in homes and factories. Though early in her career, her contributions are already shaping how robots can generalize from imperfect human input, a key step toward more adaptable and user-friendly robotic systems. Chao's work stands out for its focus on practical, real-world deployment challenges, making her a promising voice in the next generation of robotics researchers.