Papers
10
Total Citations
118
H-Index
6
About
Woo-Ri Ko is a leading researcher in social robotics, specializing in endowing robots with the social intelligence needed for natural human-robot interaction. Her work centers on machine learning for non-verbal communication, including co-speech gesture generation and adaptive behavior recognition. A key contribution is the development of end-to-end learning frameworks that allow humanoid robots to automatically learn social behaviors—such as handshakes and hugs—from human-human interaction data, moving beyond rigid, rule-based systems. Her most cited paper, "AIR-Act2Act" (2021, 36 citations), provides a crucial dataset for teaching robots these non-verbal cues. Ko also explores task intelligence through neural models of episodic memory and thought, enabling robots to reason and plan motions for complex tasks. Her recent work addresses pressing societal needs, as seen in her 2024 study on human-care robot services for the elderly, which tackles loneliness and depression. With over 100 citations across her top papers, Ko’s research is foundational for creating socially adept robots that can learn, adapt, and meaningfully engage with people in real-world settings.
Research Focus
Key Achievements
Top Papers
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- 4End-to-End Learning of Social Behaviors for Humanoid Robots8 citations · 2020
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