Jimeng Bai

Changchun University of Science and Technology

Papers

Total Citations

H-Index

About

Jimeng Bai is a rising researcher in intelligent robotics, specializing in reinforcement learning and robotic manipulation. His work focuses on overcoming critical limitations in robotic arm assembly and grasp detection—areas where traditional single-agent algorithms often struggle with convergence and reliability in complex, real-world scenarios. Bai’s most cited paper, “Multi-agent deep reinforcement learning-based robotic arm assembly research” (2025, 9 citations), proposes a novel multi-agent deep reinforcement learning framework that significantly improves coordination and success rates in assembly tasks, addressing a key bottleneck in industrial automation. His earlier work, “G-RCenterNet: Reinforced CenterNet for Robotic Arm Grasp Detection” (2024, 2 citations), introduces a reinforced CenterNet architecture that enhances both detection accuracy and real-time performance for grasping operations, directly tackling the trade-off between precision and speed. Though early in his career, Bai’s contributions are already shaping the future of autonomous manufacturing, offering scalable solutions that bridge the gap between simulation and practical deployment. His research holds promise for advancing human-robot collaboration and smart factory systems.