This paper presents an advanced robotic automation framework that combines an impedance-based compliance controller with an imitation learning network for high-precision peg-in-hole assembly. The framework is characterized by three key features. First, it employs an impedance-based compliance controller to ensure stable contact. This approach enables the robot to adapt flexibly to external contact forces, functioning like a spring-damper system to prevent potential damage. Second, domain randomization is applied to both geometric and visual properties within a high-fidelity simulation environment. This strategy effectively narrows the reality gap, enhancing robustness against environmental uncertainties and visual disturbances. Third, the framework utilizes an action-chunking-transformer (ACT) network to predict precise action sequences based on multimodal data, reducing compounding errors in trajectory generation and improving assembly success rates. Each feature is supported by specific advancements, such as real-time force feedback integration, diverse simulation scenario generation, and multimodal sensor fusion. Extensive experiments conducted in various unseen environments demonstrate the framework's effectiveness, confirming its suitability for complex assembly tasks that require high adaptability and precision under diverse conditions.

This paper proposes a high-fidelity finite element model of a permanent synchronous motor (PMSM) to predict electromagnetic responses. The proposed method aims to generate electromagnetic responses from the PMSM under various operational conditions-including normal and faulty conditions-by coupling several partial differential equations governing the electromagnetics of a PMSM. The rotor eccentricity is considered to be a representative fault of a PMSM, which has electromagnetic characteristics that differ from the healthy state of a PMSM. Note that eccentricity is the most frequent fault during PMSM operation. Therefore, the proposed model could replicate the defected torque responses of an actual motor system. The effectiveness of the proposed model is validated using measurements from a PMSM test bench. Quantitative comparison reveals that the proposed model could replicate both the transient- and steady-state torque responses of the PMSM of interest at a variety of operational conditions, including a faulty status. The proposed model could be used to generate virtual electromagnetic responses of a PMSM, which could be used for data-driven fault detection methods of electric motor systems.