Papers
4
Total Citations
46
H-Index
4
About
Bonnie Guan is an emerging robotics and human-machine interface researcher whose work sits at the intersection of electromyography (EMG)-based control, machine learning, and robotic telemanipulation. Her research focuses on developing intuitive, hands-free interfaces that decode human motion and intention to enable seamless control of robotic arm-hand systems — a challenge with profound implications for assistive technology, industrial automation, and remote operations in hazardous environments. Guan's most cited work, "On EMG Based Dexterous Robotic Telemanipulation" (2022, 25 citations), offers a comprehensive assessment of machine learning techniques, feature extraction methods, and shared control schemes for muscle-machine interfaces. Building on this foundation, she has advanced semi-autonomous telemanipulation frameworks incorporating potential fields, and explored affordance-based control architectures that make robotic systems more responsive and collaborative. Her more recent exploration of transformer architectures applied to Lightmyography signals for multi-grasp classification signals her interest in cutting-edge deep learning approaches to gesture recognition. With a growing body of work accumulating over 46 citations in just a few years, Guan is establishing herself as a promising voice in human-robot interaction research, consistently pushing toward more naturalistic and reliable interfaces between human operators and robotic systems.
Research Focus
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Top Papers
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