Papers
10
Total Citations
157
H-Index
6
About
Ruoshi Wen is a robotics researcher whose work sits at the intersection of human-robot interaction, dexterous manipulation, and intelligent control systems. With a research portfolio spanning soft robotics, electromyography-based sensing, and motion retargeting, Wen has made meaningful contributions to the challenge of enabling robots to replicate the nuanced capabilities of human hands. Among Wen's most recognized contributions is the development of sEMG-based force prediction for high-precision grasping of fragile and deformable objects (2020, 43 citations), a non-invasive approach that harnesses human sensory-motor synergies to guide robotic contact forces with remarkable accuracy. Complementing this, Wen's work on soft pneumatic actuators for universal grippers (2018, 37 citations) addresses the longstanding rigidity limitations of industrial robotic systems. Further advancing the field, Wen has pioneered multicontact motion retargeting frameworks for high-degree-of-freedom robots (2022, 20 citations) and robust bimanual manipulation strategies enabling seamless human-robot collaboration (2023, 14 citations). Work on infinite hidden Markov models for hand movement recognition (2021, 18 citations) demonstrates Wen's facility with machine learning techniques applied to embodied robotics. Collectively, Wen's research charts a compelling path toward robots that are genuinely responsive, adaptable, and safe partners in complex manipulation tasks.
Research Focus
Key Achievements
Top Papers
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- 6A Novel Underactuated Soft Humanoid Hand For Hand Sign Language10 citations · 2019
- 7Motion recognition based on concept learning4 citations · 2017
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