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

Abstract

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 framework incorporates a segmented reward function and domain randomization techniques to enhance adaptability. A fuzzy mechanism integrates an admittance controller with DDPG, embedding expert knowledge to improve learning efficiency. Additionally, a state priority experience replay mechanism is introduced to mitigate sample priority estimation errors and accelerate exploration. Simulation results under diverse configurations demonstrate the superiority of the proposed method, achieving over 96% success rate with a 43.5% reduction in maximum contact force compared to the standard DDPG baseline. Experimental validations confirm the feasibility of the learned policy in real-world settings, successfully accomplishing the assembly task that the baseline failed to complete.