Papers
3
Total Citations
10
H-Index
2
About
Xueqian Wang is a robotics and control systems researcher whose work spans robot manipulation, cable-driven mechanisms, and reinforcement learning-based control. His research addresses some of the most persistent challenges in robotic engineering, including motion coupling, precision control, and fault tolerance in complex mechanical systems. Among his notable contributions is the D3-ARM, a high-dynamic, dexterous, and fully decoupled cable-driven robotic arm that innovatively resolves the longstanding issues of motion coupling and cable routing that typically compromise control precision in cable-transmission systems. This work, already accumulating citations since its 2025 publication, reflects Wang's commitment to advancing practical robotic hardware design. His earlier work on fault-tolerant control of robot manipulators demonstrated sophisticated use of multi-sensor switching strategies and linear-parameter-varying models to maintain system reliability under sensor failure conditions — a critical consideration for real-world deployment. More recently, Wang has expanded into data-driven approaches, contributing to offline goal-conditioned reinforcement learning for safety-critical robotic tasks, addressing the challenge of learning robust policies under constraints from limited datasets. Collectively, his research bridges theoretical control systems with applied robotics, making meaningful contributions to both the reliability and dexterity of next-generation robotic systems.
Research Focus
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Top Papers
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