ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility

• URL: http://arxiv.org/abs/2309.16909v2
• Date: Thu, 29 Feb 2024 07:05:52 GMT
• Title: ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility
• Authors: Yunsheng Tian, Karl D.D. Willis, Bassel Al Omari, Jieliang Luo, Pingchuan Ma, Yichen Li, Farhad Javid, Edward Gu, Joshua Jacob, Shinjiro Sueda, Hui Li, Sachin Chitta and Wojciech Matusik
• Abstract summary: We present ASAP, a physics-based planning approach for automatically generating a sequence for general-shaped assemblies. A search can be guided by either geometrics or graph neural networks trained on data with simulation labels. We show the superior performance of ASAP at generating physically realistic assembly sequence plans on a large dataset of hundreds of complex product assemblies.
• Score: 27.424678100675163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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