Ting Xu
Papers
1
Total Citations
7
H-Index
1
About
Ting Xu is a leading researcher in rehabilitation robotics, with a primary focus on cable-driven parallel robots (CDPRs) for therapeutic applications. Their most-cited work, "Deep Reinforcement Learning Based Cable Tension Distribution Optimization for Cable-driven Rehabilitation Robot" (2021, 7 citations), introduces a novel approach to a persistent challenge in the field: maintaining stable and safe cable tension during patient-robot interaction. By applying deep reinforcement learning, Xu developed an adaptive optimization method that dynamically adjusts tension distribution in a 3-DOF CDPR, significantly improving control precision and patient comfort. This contribution bridges the gap between advanced machine learning and practical rehabilitation engineering, offering a scalable solution for next-generation assistive devices. Xu's research demonstrates a strong commitment to translating computational innovations into tangible clinical benefits, with their work laying the groundwork for more intelligent, responsive rehabilitation systems. As the demand for personalized, robot-assisted therapy grows, Xu's integration of reinforcement learning with mechanical design positions them as a key innovator in making rehabilitation safer, more effective, and more accessible.
Research Focus
Key Achievements
Top Papers
- 1