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About

Shaodong Li is a robotics researcher focused on advancing autonomous assembly in uncertain and unstructured environments. His core contributions lie at the intersection of reinforcement learning, compliant control, and multi-sensor fusion for robotic manipulation. Li’s most influential work, "Robotic Peg-in-Hole Assembly Strategy Research Based on Reinforcement Learning Algorithm" (9 citations), introduces a variable admittance control framework that enables robots to adaptively perform precision assembly tasks without explicit programming. He further extends this capability in "Hybrid Compliant Strategy for Multiple Peg-in-Hole Assembly in Robotic Drain Line Connection" (3 citations), proposing a novel hybrid compliant device for complex, multi-step assembly sequences. More recently, Li has pioneered the integration of visual and tactile sensing with soft actor-critic (SAC) learning in "Visual–Tactile Fusion and SAC-Based Learning for Robot Peg-in-Hole Assembly in Uncertain Environments" (2 citations), addressing real-world challenges like pose deviations and environmental noise. His work also explores human-robot collaboration, using deep deterministic policy gradient (DDPG) to achieve natural, effort-saving force cooperation. With a growing citation footprint, Li is establishing himself as a key contributor to intelligent robotic assembly, bridging simulation and real-world deployment.