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About

Xiaogang Yuan is a rising researcher at the forefront of intelligent robotic manipulation, specializing in reinforcement learning (RL), human-robot collaboration, and sensor fusion for complex assembly tasks. His work tackles the fundamental challenge of enabling robots to operate effectively in uncertain, unstructured environments—a critical bottleneck in modern manufacturing. Yuan’s most impactful contribution, "Robotic Peg-in-Hole Assembly Strategy Research Based on Reinforcement Learning Algorithm" (9 citations), pioneers the use of RL-driven variable admittance control to improve assembly precision, directly addressing real-world industrial needs. He further advances the field with "SC-AIRL: Share-Critic in Adversarial Inverse Reinforcement Learning for Long-Horizon Task" (6 citations), which mitigates exploration failures in long-duration tasks, a key hurdle in imitation learning. His recent work, "Visual–Tactile Fusion and SAC-Based Learning for Robot Peg-in-Hole Assembly in Uncertain Environments" (2025), integrates multimodal sensing with state-of-the-art Soft Actor-Critic algorithms to robustly handle pose deviations and noise. Additionally, Yuan explores human-robot force cooperation using deep deterministic policy gradient (DDPG), aiming to create intuitive, effort-saving interactions for diverse operators. With a growing citation footprint and a focus on bridging theory and application, Yuan is shaping the next generation of adaptive, collaborative robotics.