Publication 2024


Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility

Fig. 1: Assembly plans generated autonomously from ASAP for a desk positioned on a rotary table including physically feasible assembly sequences, collision-free paths, gravitationally stable poses, gripper grasps, and robot arm motion.


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.

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Associated Researchers

Yunsheng Tian

Massachusetts Institute of Technology

Bassel Al Omari

University of Waterloo

Jieliang (Rodger) Luo

Sr. Principal AI Research Scientist

Pingchuan Ma

Massachusetts Institute of Technology

Yichen Li

Massachusetts Institute of Technology

Edward Gu

Massachusetts Institute of Technology

Joshua Jacob

Massachusetts Institute of Technology

Shinjiro Sueda

Texas A&M University

Wojciech Matusik

Massachusetts Institute of Technology

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