Concordant operations among automatic industrial devices (i.e., agents) play a significant role in achieving efficient smart manufacturing. Existing multiagent cooperation methods focus on centralized training with decentralized execution, which is unsuitable for the edge-based distributed industrial scenarios. To harmonize distributed devices' actions and enhance the production efficiency, in this article, we propose an Interactive-Imitation-based Distributed Coordination (IIDC) algorithm. Specifically, we leverage the generative adversarial imitation learning (GAIL) model to direct one agent's actions by following external professional demonstrations. We also adopt the self-imitation learning (SIL) model to direct one agent's potential actions by following its own previous good experiences. As the imperfect demonstrations in the GAIL-based interagent imitation process may degrade the imitation accuracy, we present a confidence-based matching method to reduce the gap between the professional and imitative behaviors. Furthermore, during the SIL-based intra-agent imitation process, the rewardless explorations also lead to nonoptimal imitation policy. We then present a Stein variational policy gradient based self-imitation method to learn an expected optimal policy. We validate the IIDC algorithm's effectiveness via the sequential assembly task. Evaluation results demonstrate that the IIDC algorithm can enhance the production efficiency evidently.