Evaluating Data-driven Performances of Mixed Integer Bilinear Formulations for Book Placement Planning
<|reference_start|>Evaluating Data-driven Performances of Mixed Integer Bilinear Formulations for Book Placement Planning: Mixed integer bilinear programs (MIBLPs) offer tools to resolve robotics motion planning problems with orthogonal rotation matrices or static moment balance, but require long solving times. Recent work utilizing data-driven methods has shown potential to overcome this issue allowing for applications on larger scale problems. To solve mixed-integer bilinear programs online with data-driven methods, several re-formulations exist including mathematical programming with complementary constraints (MPCC), and mixed-integer programming (MIP). In this work, we compare the data-driven performances of various MIBLP reformulations using a book placement problem that has discrete configuration switches and bilinear constraints. The success rate, cost, and solving time are compared along with non-data-driven methods. Our results demonstrate the advantage of using data-driven methods to accelerate the solving speed of MIBLPs, and provide references for users to choose the suitable re-formulation.<|reference_end|>
@article{lin2024evaluating,
    title={Evaluating Data-driven Performances of Mixed Integer Bilinear
  Formulations for Book Placement Planning},
    author={Xuan Lin, Gabriel Ikaika Fernandez, Dennis Hong},
    journal={arXiv preprint arXiv:2406.04616},
    year={2024},
    doi={10.1109/UR61395.2024.10597466},
    archivePrefix={arXiv},
    eprint={2406.04616},
    primaryClass={cs.RO}
}