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Total Citations
2
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About
Jiandong Lv is a prominent researcher in the field of robotics and artificial intelligence, with a primary focus on intelligent motion planning and autonomous control systems. His work centers on developing advanced deep reinforcement learning (DRL) frameworks to address critical challenges in robotic arm trajectory planning, particularly in complex, dynamic environments where real-time adaptability is essential. Lv’s major contribution lies in pioneering self-optimizing replay mechanisms that enable robotic systems to learn and refine strategies without reliance on human expert demonstrations, overcoming the high-dimensional state spaces and uncertainties inherent in such tasks. His most-cited paper, “Robotic Arm Trajectory Planning in Dynamic Environments Based on Self-Optimizing Replay Mechanism” (2025), has garnered 2 citations and represents a significant step toward fully autonomous robotic manipulation. This work is notable for its innovative approach to bridging the gap between simulation and real-world deployment, offering a scalable solution for industrial automation and service robotics. Lv’s research continues to influence the next generation of adaptive robotic systems, making him a key figure in advancing DRL-based control.
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Top Papers
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