Wanxing Tang

Jiangxi University of Science and Technology

Papers

Total Citations

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About

Wanxing Tang is a leading researcher in robotics and intelligent control systems, with a primary focus on dual-arm robot manipulation and human-robot interaction in complex environments. Their most significant contribution is the development of a deep reinforcement learning-based trajectory planning framework for dual-arm robots, as detailed in their highly cited 2022 paper. This work addresses the critical challenge of enabling robots to safely approach patients in cluttered medical settings, where obstacles like human bodies and beds create collision risks. By integrating advanced reinforcement learning algorithms, Tang's approach allows robotic arms to autonomously navigate these complex spaces, marking a major advancement in assistive robotics. With 30 citations, this paper has quickly become a foundational reference for researchers working on safe robot motion planning in healthcare applications. Tang's work bridges the gap between theoretical reinforcement learning and practical robotic deployment, offering tangible solutions for real-world medical environments. Their research continues to influence the development of more adaptive and collision-aware robotic systems, positioning them as a key innovator in the field of intelligent robotics.