@inproceedings{22b0642b0ad64f599ba86425982f10e5,
title = "Universal Plans: One Action Sequence to Solve Them All!",
abstract = "This paper introduces the notion of a universal plan, which when executed, is guaranteed to solve all planning problems in a category, regardless of the obstacles, initial state, and goal set. Such plans are specified as a deterministic sequence of actions that are blindly applied without any sensor feedback. Thus, they can be considered as pure exploration in a reinforcement learning context, and we show that with basic memory requirements, they even yield optimal plans. Building upon results in number theory and theory of automata, we provide universal plans both for discrete and continuous (motion) planning and prove their (semi)completeness. The concepts are applied and illustrated through simulation studies, and several directions for future research are sketched.",
keywords = "Discrete planning, Graph exploration, Maze searching, Motion planning, Normal numbers, Planning algorithms",
author = "Timperi, \{Kalle G.\} and LaValle, \{Alexander J.\} and LaValle, \{Steven M.\}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; 16th International Workshop on the Algorithmic Foundations of Robotics, WAFR 2024 ; Conference date: 07-10-2024 Through 09-10-2024",
year = "2026",
doi = "10.1007/978-3-032-09970-9\_12",
language = "English (US)",
isbn = "9783032099693",
series = "Springer Proceedings in Advanced Robotics",
publisher = "Springer",
pages = "221--239",
editor = "Amato, \{Nancy M.\} and Katie Driggs-Campbell and Marco Morales and Chinwe Ekenna and Marco Morales and O{\textquoteright}Kane, \{Jason M.\}",
booktitle = "Algorithmic Foundations of Robotics XVI - Proceedings of the 15th Workshop on the Algorithmic Foundations of Robotics",
address = "Germany",
}