Learn2Decompose: Learning Problem Decomposition for Efficient Task and Motion Planning
<|reference_start|>Learn2Decompose: Learning Problem Decomposition for Efficient Task and Motion Planning: We focus on designing efficient Task and Motion Planning (TAMP) approach for long-horizon manipulation tasks involving multi-step manipulation of multiple objects. TAMP solvers typically require exponentially longer planning time as the planning horizon and the number of environmental objects increase. To address this challenge, we first propose Learn2Decompose, a Learning from Demonstrations (LfD) approach that learns embedding task rules from demonstrations and decomposes the long-horizon problem into several subproblems. These subproblems require planning over shorter horizons with fewer objects and can be solved in parallel. We then design a parallelized hierarchical TAMP framework that concurrently solves the subproblems and concatenates the resulting subplans for the target task, significantly improving the planning efficiency of classical TAMP solvers. The effectiveness of our proposed methods is validated in both simulation and real-world experiments.<|reference_end|>
@article{zhang2024learn2decompose:,
    title={Learn2Decompose: Learning Problem Decomposition for Efficient Task and
  Motion Planning},
    author={Yan Zhang, Amirreza Razmjoo, Sylvain Calinon},
    journal={arXiv preprint arXiv:2408.06843},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06843},
    primaryClass={cs.RO}
}