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 could be found at https://scanet-iros2024.github.io/.

翻译：机器人与三维视觉领域的自主装配任务面临重大挑战，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，但在需要长程规划的任务中往往难以取得理想效果。同时我们观察到，引入自校正模块能够部分缓解此类问题。基于此，我们提出单步装配误差校正任务，旨在识别并修正错误装配的组件。为支持该领域研究，我们构建了乐高误差校正装配数据集（LEGO-ECA），包含装配步骤的手册图像及装配失败案例。此外，我们提出自校正装配网络（SCANet）——一种解决该任务的新方法。SCANet将已装配组件作为查询对象，通过判断其在手册图像中的正确性并提供必要的修正方案。最后，我们利用SCANet对MEPNet的装配结果进行校正。实验结果表明，SCANet能够有效识别并修正MEPNet的错误装配结果，显著提升装配正确率。代码与数据集可通过https://scanet-iros2024.github.io/获取。