Design of imitation learning fusion algorithm for mobile robotic arm control

Abstract

Despite the considerable potential of artificial intelligence technology in industrial applications, it still faces challenges such as high data requirements, limited generalization capabilities, and concerns with safety and stability. These issues become particularly prominent in the execution of precision operations such as opening button-lock cabinet doors during robotic inspection tasks. The tasks involve complex environmental perception, dynamically changing operational settings, high stability requirements, and the challenge of generalization, all of which necessitate the integration of multiple advanced algorithms into a fusion system design. This research focuses on the system design and algorithm integration challenges for mobile robotic arms in inspection tasks, proposing a Deep Meta-Imitation Learning (DMIL) algorithm that combines deep meta-learning with imitation learning (IL) to enhance the adaptability and efficiency of mobile robotic arms in such tasks. Expert trajectories were generated and analyzed in the CoppeliaSim simulation environment, and actual interactions were evaluated. The study employs a deep learning-based 6D pose estimation method to determine the position and orientation of button locks, with visual recognition of key operational reference points. In the imitation learning phase, the operational strategies of the robotic arm are enhanced by combining Adversarial Inverse Reinforcement Learning (AIRL) and Variable Impedance Control (VIC) technologies, supported by expert-guided trajectories and force feedback data from real-world environments. Additionally, a Latent Embedding Optimization (LEO) module is introduced into the deep meta-learning framework, enabling the model to quickly adapt to new tasks, significantly improving its generalization ability. Experiments were conducted in the CoppeliaSim simulation environment and on a mobile robotic arm platform, focusing on the recognition process, trajectory planning, and compliance control management. To assess the algorithm's effectiveness, three button-lock cabinet door-opening tasks of varying difficulty were executed. The experimental results demonstrate that the mobile robotic arm was able to accurately locate and compliantly open multiple button locks, showcasing the practicality and feasibility of this approach in advancing robotic precision operations in inspection tasks.