Autonomous assembly in robotics and 3D vision presents significant challenges, particularly in ensuring assembly correctness. Presently, predominant methods such as MEPNet focus on assembling components based on manually provided images. However, these approaches often fall short in achieving satisfactory results for tasks requiring long-term planning. Concurrently, we observe that integrating a self-correction module can partially alleviate such issues. Motivated by this concern, we introduce the single-step assembly error correction task, which involves identifying and rectifying misassembled components. To support research in this area, we present the LEGO Error Correction Assembly Dataset (LEGO-ECA), comprising manual images for assembly steps and instances of assembly failures. Additionally, we propose the Self-Correct Assembly Network (SCANet), a novel method to address this task. SCANet treats assembled components as queries, determining their correctness in manual images and providing corrections when necessary. Finally, we utilize SCANet to correct the assembly results of MEPNet. Experimental results demonstrate that SCANet can identify and correct MEPNet's misassembled results, significantly improving the correctness of assembly. Our code and dataset are available at https://github.com/Yaser-wyx/SCANet.

翻译：机器人自主装配与3D视觉领域面临重大挑战，尤其在确保装配正确性方面。当前主流方法如MEPNet主要依赖人工提供的图像进行组件装配，但在需要长期规划的任务中往往难以取得理想效果。同时，我们观察到引入自校正模块可部分缓解此类问题。基于这一考量，本文提出单步装配误差校正任务，旨在识别并修正装配错误的组件。为支撑该领域研究，我们构建了乐高误差校正装配数据集（LEGO-ECA），包含装配步骤的人工图像及装配失败实例。进一步，我们提出自校正装配网络（SCANet）这一创新方法以解决该任务。SCANet将已装配组件视为查询对象，通过人工图像判定其装配正确性，并在必要时提供修正方案。最终，我们应用SCANet对MEPNet的装配结果进行校正。实验表明，SCANet能有效识别并修正MEPNet的装配错误，显著提升装配正确率。相关代码与数据集已开源至https://github.com/Yaser-wyx/SCANet。