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About

Jingli Du is a leading researcher in robotics and artificial intelligence, with a primary focus on the mechanics and control of cable-driven parallel robots (CDPRs) and reinforcement learning (RL). Du’s major contributions include pioneering three-dimensional static and dynamic stiffness analyses of CDPRs that account for non-negligible cable mass and elasticity—a critical advancement for high-precision applications. This work, published in 2017 and garnering 16 citations, provides optimization models for cable tensions and lengths using the fminimax solver, enabling more accurate and robust robotic systems. Du has also made significant strides in vibration suppression for redundantly controlled CDPRs (2024, 10 citations), enhancing operational stability. In the realm of AI, Du introduced HCS-R-HER, a hierarchical reinforcement learning algorithm that leverages cross-subtask rainbow hindsight experience replay (2023, 10 citations), and developed a model-based exploration strategy to accelerate deterministic policy training (2023, 2 citations). These innovations address fundamental challenges in RL, such as balancing exploitation and exploration. With a growing citation impact and a portfolio bridging mechanical engineering and machine learning, Du’s work is shaping the future of intelligent robotic systems.