The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together. In this paper, we present ASAP, a physics-based planning approach for automatically generating such a sequence for general-shaped assemblies. ASAP accounts for gravity to design a sequence where each sub-assembly is physically stable with a limited number of parts being held and a support surface. We apply efficient tree search algorithms to reduce the combinatorial complexity of determining such an assembly sequence. The search can be guided by either geometric heuristics or graph neural networks trained on data with simulation labels. Finally, we show the superior performance of ASAP at generating physically realistic assembly sequence plans on a large dataset of hundreds of complex product assemblies. We further demonstrate the applicability of ASAP on both simulation and real-world robotic setups. Project website: asap.csail.mit.edu
@article{arxiv.2309.16909,
title = {ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility},
author = {Yunsheng Tian and Karl D. D. Willis and Bassel Al Omari and Jieliang Luo and Pingchuan Ma and Yichen Li and Farhad Javid and Edward Gu and Joshua Jacob and Shinjiro Sueda and Hui Li and Sachin Chitta and Wojciech Matusik},
journal= {arXiv preprint arXiv:2309.16909},
year = {2024}
}