Integrating Visual Reasoning Localization with Reinforcement Learning for 3C Assembly

In 3C (Computer, Communication, and Consumer Electronics) flexible assembly tasks, due to the frequent updates and the increasing demand for intelligence, traditional automated assembly methods struggle to meet the requirements for high precision and adaptability. To address these challenges, this paper proposes an assembly method that combines visual reasoning localization with deep reinforcement learning. The method improves the reward function by integrating visual reasoning results with reinforcement learning and trains the model in a virtual environment. Through sim-to-real transfer, the assembly strategies learned in the virtual environment are applied to real-world scenarios, significantly enhancing the assembly accuracy and success rate in real-world settings. Validation on a 3C front camera assembly task demonstrates an assembly accuracy of 0.15 mm and a success rate of 97%.