A World Model for Assembly Sequence Planning

This paper proposes a world model for planning assembly sequences for LEGO structures. The model predicts placement feasibility by looking ahead in a learned latent space, without invoking a physical simulator inside the look-ahead search. The primary technical challenge lies in judging whether committing to a placement leaves the remaining structure buildable, which requires reasoning over future steps. To address this, we represent the assembly with a multi-granularity heterogeneous graph, in which a master node aggregates the overall geometry of the assembled structure as a set of anchor tokens, while a brick-level sub-graph carries the interlocking topology and the static attributes of each brick. A latent dynamics model advances the anchor tokens locally after each placement, which lets the world model roll the geometry forward over several steps and anticipate whether a candidate placement obstructs the assembly of the remaining bricks. We validate the framework on held-out test structures from the StableText2Lego benchmark, comparing it against ablations and recent assembly-sequence methods. Our planner achieves near-oracle solutions while issuing the fewest exact-oracle calls per brick and avoiding the later-infeasible placements incurred by the baselines.