The Limits of Learning and Planning: Minimal Sufficient Information Transition Systems
Sakcak, Basak; Weinstein, Vadim; LaValle, Steven M. (2022-12-15)
Springer
15.12.2022
Sakcak, B., Weinstein, V., LaValle, S.M. (2023). The Limits of Learning and Planning: Minimal Sufficient Information Transition Systems. In: LaValle, S.M., O’Kane, J.M., Otte, M., Sadigh, D., Tokekar, P. (eds) Algorithmic Foundations of Robotics XV. WAFR 2022. Springer Proceedings in Advanced Robotics, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-031-21090-7_16
https://rightsstatements.org/vocab/InC/1.0/
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG.
https://rightsstatements.org/vocab/InC/1.0/
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG.
https://rightsstatements.org/vocab/InC/1.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2023031331296
https://urn.fi/URN:NBN:fi-fe2023031331296
Tiivistelmä
Abstract
In this paper, we view a policy or plan as a transition system over a space of information states that reflect a robot’s or other observer’s perspective based on limited sensing, memory, computation, and actuation. Regardless of whether policies are obtained by learning algorithms, planning algorithms, or human insight, we want to know the limits of feasibility for given robot hardware and tasks. Toward the quest to find the best policies, we establish in a general setting that minimal information transition systems (ITSs) exist up to reasonable equivalence assumptions, and are unique under some general conditions. We then apply the theory to generate new insights into several problems, including optimal sensor fusion/filtering, solving basic planning tasks, and finding minimal representations for feasible policies.
In this paper, we view a policy or plan as a transition system over a space of information states that reflect a robot’s or other observer’s perspective based on limited sensing, memory, computation, and actuation. Regardless of whether policies are obtained by learning algorithms, planning algorithms, or human insight, we want to know the limits of feasibility for given robot hardware and tasks. Toward the quest to find the best policies, we establish in a general setting that minimal information transition systems (ITSs) exist up to reasonable equivalence assumptions, and are unique under some general conditions. We then apply the theory to generate new insights into several problems, including optimal sensor fusion/filtering, solving basic planning tasks, and finding minimal representations for feasible policies.
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