Christina Baek
Papers
2
Total Citations
46
H-Index
2
About
Christina Baek’s research lies at the intersection of robotics, deep learning, and human-robot interaction, with a focus on developing intelligent, socially-aware systems for service robots. Her most cited work, “Robust Human Following by Deep Bayesian Trajectory Prediction for Home Service Robots” (2018, 38 citations), addresses a critical challenge in assistive robotics: enabling robots to reliably follow a person despite dynamic environments and temporary loss of the target. By integrating deep Bayesian trajectory prediction, Baek’s approach significantly improves robustness and reacquisition capabilities, advancing the practicality of home service robots. In her related work, “Perception-Action-Learning System for Mobile Social-Service Robots Using Deep Learning” (2018, 8 citations), she introduces a unified framework that leverages state-of-the-art deep learning across perception, action, and learning modules. This system demonstrates fast, robust performance in social service tasks, highlighting her ability to bridge theoretical advances with real-world deployment. Baek’s contributions are foundational for next-generation service robots that can safely and intuitively collaborate with humans in everyday environments.
Research Focus
Key Achievements
Top Papers
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- 2