“Soy: An Efficient MILP Solver for Piecewise-Affine Systems” by Haoze Wu, Min Wu, Dorsa Sadigh, and Clark Barrett. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '23), Oct. 2023, pp. 6281-6288. Detroit, MI, USA.
Piecewise-affine (PWA) systems are widely used for modeling and control of robotics problems including modeling contact dynamics. A common approach is to encode the control problem of the PWA system as a Mixed-Integer Convex Program (MICP), which can be solved by general-purpose off-the-shelf MICP solvers. To mitigate the scalability challenge of solving these MICP problems, existing work focuses on devising efficient and strong formulations of the problems, while less effort has been spent on exploiting their specific structure to develop specialized solvers. The latter is the theme of our work. We focus on efficiently handling one-hot constraints, which are particularly relevant when encoding PWA dynamics. We have implemented our techniques in a tool, Soy, which organically integrates logical reasoning, arithmetic reasoning, and stochastic local search. For a set of PWA control benchmarks, Soy solves more problems, faster, than two state-of-the-art MICP solvers.
BibTeX entry:
@inproceedings{WWS+23,
author = {Haoze Wu and Min Wu and Dorsa Sadigh and Clark Barrett},
title = {Soy: An Efficient {MILP} Solver for Piecewise-Affine Systems},
booktitle = {2023 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS '23)},
pages = {6281--6288},
publisher = {IEEE},
month = oct,
year = {2023},
doi = {10.1109/IROS55552.2023.10342011},
note = {Detroit, MI, USA},
url = {https://doi.org/10.1109/IROS55552.2023.10342011}
}
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