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arXiv · 2007.08451

Specification mining and automated task planning for autonomous robots based on a graph-based spatial temporal logic

Zhiyu Liu·Meng Jiang·Hai Lin
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Abstract

We aim to enable an autonomous robot to learn new skills from demo videos and use these newly learned skills to accomplish non-trivial high-level tasks. The goal of developing such autonomous robot involves knowledge representation, specification mining, and automated task planning. For knowledge representation, we use a graph-based spatial temporal logic (GSTL) to capture spatial and temporal information of related skills demonstrated by demo videos. We design a specification mining algorithm to generate a set of parametric GSTL formulas from demo videos by inductively constructing spatial terms and temporal formulas. The resulting parametric GSTL formulas from specification mining serve as a domain theory, which is used in automated task planning for autonomous robots. We propose an automatic task planning based on GSTL where a proposer is used to generate ordered actions, and a verifier is used to generate executable task plans. A table setting example is used throughout the paper to illustrate the main ideas.

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Zhiyu Liu, Meng Jiang, Hai Lin. 2020-07-16. Specification mining and automated task planning for autonomous robots based on a graph-based spatial temporal logic. https://arxiv.org/abs/2007.08451

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