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Title: Rearrangement Planning for General Part Assembly
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.  more » « less
Award ID(s):
2037101
PAR ID:
10483038
Author(s) / Creator(s):
; ;
Editor(s):
Tan, Jie; Toussaint, Marc; Darvish, Kourosh
Publisher / Repository:
Proceedings of Machine Learning Research, MLResearchPress, https://proceedings.mlr.press/v229/li23a.html
Date Published:
Journal Name:
Proceedings of the 7th Conference on Robot Learning
Volume:
229
ISSN:
2640-3498
Page Range / eLocation ID:
127-143
Subject(s) / Keyword(s):
robotic assembly pose estimation 3D perception
Format(s):
Medium: X
Location:
Atlanta, GA
Sponsoring Org:
National Science Foundation
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