Minyoung Hwang
Papers
1
Total Citations
6
H-Index
1
About
Minyoung Hwang is a rising researcher in embodied artificial intelligence and human-robot interaction, with a focus on aligning robotic behavior with diverse human preferences. Their most-cited work, "Promptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferences" (2024, 6 citations), introduces a novel framework that enables efficient customization of robotic agents by translating human preferences into multi-objective reward functions. This contribution addresses a critical gap in embodied AI—how to make robots adaptable to individual user needs without extensive retraining. Hwang’s approach leverages prompt-based personalization, allowing non-expert users to guide robot behavior intuitively. While still early in their career, Hwang’s work has already garnered attention for its practical implications in assistive robotics and human-centered AI. Their research bridges reinforcement learning and user modeling, offering a scalable path toward more responsive and socially aware autonomous systems. Hwang’s contributions are particularly notable for tackling the underexplored challenge of preference diversity in robotics, setting the stage for future advances in personalized embodied agents.
Research Focus
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Top Papers
- 1