  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
