  Automating the assembly of objects from their parts is a complex problem with
innumerable applications in manufacturing, maintenance, and recycling. Unlike
existing research, which is limited to target segmentation, pose regression, or
using fixed target blueprints, our work presents a holistic multi-level
framework for part assembly planning consisting of part assembly sequence
inference, part motion planning, and robot contact optimization. We present the
Part Assembly Sequence Transformer (PAST) -- a sequence-to-sequence neural
network -- to infer assembly sequences recursively from a target blueprint. We
then use a motion planner and optimization to generate part movements and
contacts. To train PAST, we introduce D4PAS: a large-scale Dataset for Part
Assembly Sequences (D4PAS) consisting of physically valid sequences for
industrial objects. Experimental results show that our approach generalizes
better than prior methods while needing significantly less computational time
for inference.
