Ruohan Wang
Papers
12
Total Citations
256
H-Index
6
About
Ruohan Wang is an emerging robotics and human-machine interaction researcher whose work sits at the intersection of intelligent sensing, safe human-robot collaboration, and healthcare robotics. His most influential contribution, "Machine Learning-Enabled Tactile Sensor Design for Dynamic Touch Decoding" (2023, 147 citations), pioneered an inverse design strategy for flexible skin-like sensors, challenging conventional trial-and-error approaches and establishing a new paradigm for sensor development in healthcare and prosthetics. Building on this foundation, Wang has advanced robot perception through attention-based deep learning for inertial motion recognition in collaborative environments (53 citations) and developed large-area digital twin-driven robot skin systems tailored for Healthcare 4.0 applications. His research extends into teleoperative robotics, including wearable upper-limb exoskeletons with force feedback and phygital twin-driven robot avatars enabling intercontinental teleoperation between China and Sweden. Wang has also addressed real-world healthcare challenges directly, designing medical assistive robots deployed in COVID-19 isolation wards to support patient well-being and reduce clinical exposure. Collectively, his portfolio reflects a coherent vision: creating safer, smarter, and more intuitive interfaces between humans and robots across industrial, medical, and collaborative domains.
Research Focus
Key Achievements
Top Papers
- 1Machine Learning‐Enabled Tactile Sensor Design for Dynamic Touch Decoding147 citations · 2023
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