Xiaoci Zhang
Papers
1
Total Citations
3
H-Index
1
About
Xiaoci Zhang is a leading researcher in soft robotics, with a focus on proprioception and model-based shape reconstruction. Their work addresses a critical challenge in the field: enabling soft robots to autonomously sense and adapt to their own deformations in unstructured environments. Zhang’s most cited paper, “Model-Based 3D Shape Reconstruction of Soft Robots via Distributed Strain Sensing” (2025), introduces a novel approach that overcomes the limitations of traditional machine learning-based sensorization methods. By leveraging distributed strain sensing and physics-based models, this work provides a more robust and generalizable solution for real-time shape estimation, a key enabler for safe and autonomous robotic behaviors. Although early in its trajectory, the paper has already garnered 3 citations, signaling its growing influence. Zhang’s contributions are paving the way for more intelligent and adaptive soft robotic systems, with potential applications in medical devices, search-and-rescue, and human-robot interaction. Their research stands out for its elegant fusion of sensing, modeling, and control, offering a promising path toward truly autonomous soft robots.
Research Focus
Key Achievements
Top Papers
- 1