Rearrangement Planning for General Part Assembly

Yulong Li, Andy Zeng, Shuran Song

Keywords: robotic assembly, pose estimation, 3D perception

TL;DR: We propose to investigate general part assembly, the task of creating novel target assemblies with unseen part shapes, and introduce General Part Assembly Transformer (GPAT) for the task.

Abstract: Most successes in autonomous robotic assembly have been restricted to single target or category. We propose to investigate general part assembly, the task of creating novel target assemblies with unseen part shapes. As a fundamental step to a general part assembly system, we tackle the task of determining the precise poses of the parts in the target assembly, which we term “rearrangement planning”. We present General Part Assembly Transformer (GPAT), a transformer-based model architecture that accurately predicts part poses by inferring how each part shape corresponds to the target shape. Our experiments on both 3D CAD models and real-world scans demonstrate GPAT’s generalization abilities to novel and diverse target and part shapes.

Student First Author: yes

Instructions: I have read the instructions for authors (https://corl2023.org/instructions-for-authors/)

Video: https://general-part-assembly.github.io/figs/video.mp4

Website: https://general-part-assembly.github.io

Code: https://github.com/real-stanford/gpat