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arXiv · 2103.00589

Learning Symbolic Operators for Task and Motion Planning

Tom Silver·Rohan Chitnis·Joshua Tenenbaum·Leslie Pack Kaelbling·Tomas Lozano-Perez
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Abstract

Robotic planning problems in hybrid state and action spaces can be solved by integrated task and motion planners (TAMP) that handle the complex interaction between motion-level decisions and task-level plan feasibility. TAMP approaches rely on domain-specific symbolic operators to guide the task-level search, making planning efficient. In this work, we formalize and study the problem of operator learning for TAMP. Central to this study is the view that operators define a lossy abstraction of the transition model of a domain. We then propose a bottom-up relational learning method for operator learning and show how the learned operators can be used for planning in a TAMP system. Experimentally, we provide results in three domains, including long-horizon robotic planning tasks. We find our approach to substantially outperform several baselines, including three graph neural network-based model-free approaches from the recent literature. Video: https://youtu.be/iVfpX9BpBRo Code: https://git.io/JCT0g

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Tom Silver, Rohan Chitnis, Joshua Tenenbaum, Leslie Pack Kaelbling, Tomas Lozano-Perez. 2021-02-28. Learning Symbolic Operators for Task and Motion Planning. https://arxiv.org/abs/2103.00589

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