Fuzzy Fusion Control Strategy With Efficient Deep Deterministic Policy Gradient for Robotic Peg-in-Hole Assembly

The robotic peg-in-hole assembly task remains challenging. Traditional force control methods struggle with complex parameter identification and contact state analysis, while deep reinforcement learning(DRL) suffers from low efficiency and poor adaptability. To address these shortcomings and to capitalize on the strengths of both, this paper presents a fuzzy fusion control strategy and improved Deep Deterministic Policy Gradient(DDPG) method to achieve efficient exploration. The proposed framewor