Feedback Deep Deterministic Policy Gradient with Fuzzy Reward for Robotic Multiple Peg-in-Hole Assembly Tasks

The automatic completion of multiple peg-in-hole assembly tasks by robots remains a formidable challenge because the traditional control strategies require a complex analysis of the contact model. In this paper, the assembly task is formulated as a Markov decision process, and a model-driven deep deterministic policy gradient algorithm is proposed to accomplish the assembly task through the learned policy without analyzing the contact states. In our algorithm, the learning process is driven by a simple traditional force controller. In addition, a feedback exploration strategy is proposed to ensure that our algorithm can efficiently explore the optimal assembly policy and avoid risky actions, which can address the data efficiency and guarantee stability in realistic assembly scenarios. To improve the learning efficiency, we utilize a fuzzy reward system for the complex assembly process. Then, simulations and realistic experiments of a dual peg-in-hole assembly demonstrate the effectiveness of the proposed algorithm. The advantages of the fuzzy reward system and feedback exploration strategy are validated by comparing the performances of different cases in simulations and experiments.