Yorie Nakahira
Papers
2
Total Citations
7
H-Index
2
About
Yorie Nakahira is a rising leader in the field of safe and optimal control for autonomous systems, with a particular focus on human-robot collaboration and stochastic dynamics. Her research bridges the critical gap between theoretical control theory and real-world deployment, tackling challenges in robotic manipulation, autonomous driving, and proactive human-robot interaction. Nakahira’s most cited work, “Towards Proactive Safe Human-Robot Collaborations via Data-Efficient Conditional Behavior Prediction” (2024, 5 citations), introduces a novel framework that enables robots to anticipate and adapt to human actions even when training data is sparse—a significant departure from traditional observational models. This work is foundational for creating truly collaborative robots that can operate safely in unpredictable environments. In her complementary paper, “Physics-Informed Representation and Learning: Control and Risk Quantification” (2024, 2 citations), she develops efficient methods for finding optimal and safe control policies in high-dimensional stochastic systems, a problem of central importance for safety-critical applications. Through her innovative integration of physics-informed learning with rigorous risk quantification, Nakahira is paving the way for more reliable, data-efficient, and proactive autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 2