Yibo Yuan

Shandong University of Science and Technology

Papers

Total Citations

H-Index

About

Yibo Yuan is a rising researcher in the field of soft robotics and tactile sensing, with a focus on developing intelligent, flexible systems that bridge the gap between human perception and machine interaction. Their work centers on creating soft sensor technologies that mimic the human sense of touch, enabling robots to perceive force positioning and object shape with remarkable fidelity. Yuan’s most-cited paper, “Learning the human perceptions of touch force positioning and object shape using a soft optical fiber tactile sensing pad” (2024, 8 citations), introduces a novel approach that combines optical fiber sensors with machine learning to interpret tactile data, offering a pathway toward more intuitive human-robot collaboration. Another notable contribution, “Intelligent soft self-twisted shape sensor” (2023, 3 citations), demonstrates an innovative self-twisting mechanism for shape detection, expanding the capabilities of soft robotics in unstructured environments. Though early in their career, Yuan’s work has already garnered attention for its potential applications in prosthetics, wearable devices, and interactive systems. Their research stands out for its emphasis on learning from human tactile perception, promising to make robots more responsive and adaptive in real-world scenarios.