Dan Dan

Papers

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About

Dan Dan’s research lies at the intersection of human-robot interaction, adaptive control, and intelligent robotics for assistive applications. Her most influential work, “Model Reference Adaptive Impedance Control for Physical Human-Robot Interaction” (2016, 19 citations), introduces a novel dual-loop control framework that ensures stability and task performance during physical collaboration. The inner loop uses a neuroadaptive controller to learn robot dynamics online, enabling the robot to behave like a prescribed impedance model without task-specific information. The outer loop adapts this impedance to account for human operator dynamics, improving joint task performance. This non-standard application of model reference adaptive control delivers both adaptive impedance characteristics and assistive inputs, validated through simulations of repetitive point-to-point motions. Dan has also explored cognitive robotics in “Cognitive Emotion Model for Eldercare Robot in Smart Home” (2015, 4 citations), combining Gabor filters, Local Binary Patterns, and k-Nearest Neighbors for facial emotion recognition in smart home environments. Her work bridges theoretical control advances with practical human-centered robotics, offering significant insights for researchers developing safe, adaptive, and emotionally aware robotic systems for healthcare and collaborative tasks.