  Assembly planning is the core of automating product assembly, maintenance,
and recycling for modern industrial manufacturing. Despite its importance and
long history of research, planning for mechanical assemblies when given the
final assembled state remains a challenging problem. This is due to the
complexity of dealing with arbitrary 3D shapes and the highly constrained
motion required for real-world assemblies. In this work, we propose a novel
method to efficiently plan physically plausible assembly motion and sequences
for real-world assemblies. Our method leverages the assembly-by-disassembly
principle and physics-based simulation to efficiently explore a reduced search
space. To evaluate the generality of our method, we define a large-scale
dataset consisting of thousands of physically valid industrial assemblies with
a variety of assembly motions required. Our experiments on this new benchmark
demonstrate we achieve a state-of-the-art success rate and the highest
computational efficiency compared to other baseline algorithms. Our method also
generalizes to rotational assemblies (e.g., screws and puzzles) and solves
80-part assemblies within several minutes.
