Physics-Aware Combinatorial Assembly Planning using Deep Reinforcement Learning
<|reference_start|>Physics-Aware Combinatorial Assembly Planning using Deep Reinforcement Learning: Combinatorial assembly uses standardized unit primitives to build objects that satisfy user specifications. Lego is a widely used platform for combinatorial assembly, in which people use unit primitives (ie Lego bricks) to build highly customizable 3D objects. This paper studies sequence planning for physical combinatorial assembly using Lego. Given the shape of the desired object, we want to find a sequence of actions for placing Lego bricks to build the target object. In particular, we aim to ensure the planned assembly sequence is physically executable. However, assembly sequence planning (ASP) for combinatorial assembly is particularly challenging due to its combinatorial nature, ie the vast number of possible combinations and complex constraints. To address the challenges, we employ deep reinforcement learning to learn a construction policy for placing unit primitives sequentially to build the desired object. Specifically, we design an online physics-aware action mask that efficiently filters out invalid actions and guides policy learning. In the end, we demonstrate that the proposed method successfully plans physically valid assembly sequences for constructing different Lego structures. The generated construction plan can be executed in real.<|reference_end|>
@article{liu2024physics-aware,
    title={Physics-Aware Combinatorial Assembly Sequence Planning using Data-free
  Action Masking},
    author={Ruixuan Liu and Alan Chen and Weiye Zhao and Changliu Liu},
    journal={arXiv preprint arXiv:2408.10162},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10162},
    primaryClass={cs.RO cs.LG}
}