Linear planning logic: An efficient language and theorem prover for robotic task planning

In this paper, we introduce a novel logic language and theorem prover for robotic task planning. Our language, which we call Linear Planning Logic (LPL), is a fragment of linear logic whose resource-conscious semantics are well suited for reasoning with dynamic state, while its structure admits effi...

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Bibliographic Details
Published in:Proceedings - IEEE International Conference on Robotics and Automation pp. 3764 - 3770
Main Authors: Kortik, Sitar, Saranli, Uluc
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2014
Subjects:
Cognition
Encoding
Legged locomotion
Planning
Rough surfaces
Surface roughness
ISSN:1050-4729
Online Access:Get full text
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Summary:In this paper, we introduce a novel logic language and theorem prover for robotic task planning. Our language, which we call Linear Planning Logic (LPL), is a fragment of linear logic whose resource-conscious semantics are well suited for reasoning with dynamic state, while its structure admits efficient theorem provers for automatic plan construction. LPL can be considered as an extension of Linear Hereditary Harrop Formulas (LHHF), whose careful design allows the minimization of nondeterminism in proof search, providing a sufficient basis for the design of linear logic programming languages such as Lolli. Our new language extends on the expressivity of LHHF, while keeping the resulting nondeterminism in proof search to a minimum for efficiency. This paper introduces the LPL language, presents the main ideas behind our theorem prover on a smaller fragment of this language and finally provides an experimental illustration of its operation on the problem of task planning for the hexapod robot RHex.
ISSN:1050-4729
DOI:10.1109/ICRA.2014.6907404