  Assembly sequence planning (ASP) is the essential process for modern
manufacturing, proven to be NP-complete thus its effective and efficient
solution has been a challenge for researchers in the field. In this paper, we
present a graph-transformer based framework for the ASP problem which is
trained and demonstrated on a self-collected ASP database. The ASP database
contains a self-collected set of LEGO models. The LEGO model is abstracted to a
heterogeneous graph structure after a thorough analysis of the original
structure and feature extraction. The ground truth assembly sequence is first
generated by brute-force search and then adjusted manually to in line with
human rational habits. Based on this self-collected ASP dataset, we propose a
heterogeneous graph-transformer framework to learn the latent rules for
assembly planning. We evaluated the proposed framework in a series of
experiment. The results show that the similarity of the predicted and ground
truth sequences can reach 0.44, a medium correlation measured by Kendall's
$\tau$. Meanwhile, we compared the different effects of node features and edge
features and generated a feasible and reasonable assembly sequence as a
benchmark for further research. Our data set and code is available on
https://github.com/AIR-DISCOVER/ICRA\_ASP.
