Visual-Privileged Co-Learning for Industrial Board-to-Board Connectors Force-Guided Assembly Task

Abstract

Automatic assembly of board-to-board (BTB) connectors remains a significant challenge in smartphone manufacturing due to severe visual occlusion, tight assembly tolerances, and process constraints that prohibit separate visual adjustment stations. This letter proposes Visual-Privileged Co-Learning (VPCL), a novel privileged reinforcement learning (RL) algorithm specifically tailored for robust, force-guided precision assembly in partially observable environments. Leveraging additional visual privileged information only during training and relying solely on force feedback during deployment, VPCL employs a dual-actor (teacher-student) architecture within the Soft Actor-Critic (SAC) framework. To enhance teacher-student interaction in the co-learning process, VPCL introduces a Teacher Quality Loss to address modality imbalance and a probabilistic Policy Selector to mitigate data distribution shifts. Extensive comparative and ablation experiments in real-world smartphone assembly tasks demonstrate that VPCL achieves superior performance and robustness, as well as a remarkable zero-shot transfer capability, compared to existing baselines.