Papers
20
Total Citations
657
H-Index
12
About
Yue Wen is a pioneering researcher at the intersection of reinforcement learning, robotic prosthetics, and human-robot interaction, whose work is reshaping how intelligent assistive devices are personalized for individuals with limb loss. Best known for applying online reinforcement learning to automate the tuning of robotic knee prosthesis control parameters — a landmark 2019 study that has garnered over 186 citations — Wen has consistently tackled one of the field's most stubborn challenges: making wearable robots truly adaptive to individual users. His foundational contributions include introducing adaptive dynamic programming for optimal prosthesis control (2017) and developing flexible policy iteration algorithms that balance data efficiency with stability guarantees in human-in-the-loop systems. Beyond prosthetics, Wen has advanced understanding of wearer-prosthesis gait symmetry and pioneered knowledge-guided reinforcement learning frameworks for real-world robotic deployment. His research extends further into human-machine-human interaction and dyadic haptic collaboration, exploring how physically coupled individuals can accelerate motor learning and rehabilitation outcomes. With over 500 cumulative citations across his most impactful works, Wen's contributions represent a defining body of scholarship for the next generation of intelligent, personalized rehabilitation robotics.
Research Focus
Key Achievements
Top Papers
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- 9A Framework for Dyadic Physical Interaction Studies During Ankle Motor Tasks19 citations · 2021
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