Haoqi Tang
Papers
1
Total Citations
16
H-Index
1
About
Haoqi Tang is a leading researcher in intelligent robotic manipulation, with a primary focus on force control, compliant grinding, and reinforcement learning for manufacturing automation. His most impactful work, "Reinforcement-Learning-Based Robust Force Control for Compliant Grinding via Inverse Hysteresis Compensation" (2023, 16 citations), addresses a critical challenge in precision manufacturing: the high unmatched stiffness of manipulators and the difficulty in measuring actual contact force (ACF) during grinding. Tang’s key contribution lies in developing a novel framework that integrates passive compliance devices (PCDs) driven by pneumatic actuators (PACs) with reinforcement learning to achieve robust, adaptive force control. This approach effectively compensates for hysteresis, preventing workpiece surface damage—a persistent problem in traditional robotic grinding. By bridging reinforcement learning with real-world industrial applications, Tang’s work offers a practical, data-driven solution for high-precision tasks. His research is pivotal for advancing intelligent manufacturing, where safe, compliant, and accurate human-robot collaboration is essential. Tang’s contributions are shaping the next generation of adaptive robotic systems, making him a notable figure in the field of robotic control and automation.
Research Focus
Key Achievements
Top Papers
- 1