SCANet: Correcting LEGO Assembly Errors with Self-Correct Assembly Network

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.

Authors

• Yuxuan Wan
• Kaichen Zhou
• Jinhong Chen
• Hao Dong 0003

Keywords

• Three-dimensional displays
• Codes
• Manuals
• Error correction
• Planning
• Assembly
• Intelligent robots
• Assembly Errors
• LEGO Assembly
• Correct Results
• Assembly Step
• Correct Assembly
• Assembly Results
• Dataset Assembly
• Classification Task
• Assembly Process
• 2D Images
• Position Error
• Correction Process
• Current Step
• 3D Shape
• Channel Dimension
• Rotational Symmetry
• Accurate Assembly
• Rotation Error
• 3D Aggregates
• Pose Information
• Correct Pose
• Convolutional Neural Networks Backbone
• 3D Voxel
• Step Error