The Limits of Learning and Planning: Minimal Sufficient Information Transition Systems

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The Limits of Learning and Planning: Minimal Sufficient Information Transition Systems

Sakcak, Basak / Weinstein, Vadim / LaValle, Steven M.

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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Dokumentinformationen

Titel :

The Limits of Learning and Planning: Minimal Sufficient Information Transition Systems

Weitere Titelangaben:

Springer Proceedings in Advanced Robotics

Beteiligte:

LaValle, Steven M. (Herausgeber:in) / O’Kane, Jason M. (Herausgeber:in) / Otte, Michael (Herausgeber:in) / Sadigh, Dorsa (Herausgeber:in) / Tokekar, Pratap (Herausgeber:in) / Sakcak, Basak (Autor:in) / Weinstein, Vadim (Autor:in) / LaValle, Steven M. (Autor:in)

Kongress:

International Workshop on the Algorithmic Foundations of Robotics ; 2022 ; , MD, USA June 22, 2022 - June 24, 2022

Erschienen in:

Algorithmic Foundations of Robotics XV ; Kapitel : 16 ; 256-272

Springer Proceedings in Advanced Robotics ; 25

Erscheinungsdatum :

2022-12-15

Format / Umfang :

17 pages

ISBN :

978-3-031-21090-7 , 978-3-031-21089-1

ISSN :