Learning Symbolic Operators for Task and Motion Planning
- URL: http://arxiv.org/abs/2103.00589v1
- Date: Sun, 28 Feb 2021 19:08:56 GMT
- Title: Learning Symbolic Operators for Task and Motion Planning
- Authors: Tom Silver, Rohan Chitnis, Joshua Tenenbaum, Leslie Pack Kaelbling,
Tomas Lozano-Perez
- Abstract summary: integrated task and motion planners (TAMP) 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.
We 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.
- Score: 29.639902380586253
- License: http://creativecommons.org/licenses/by/4.0/
- 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 the underlying 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 based on recent work. Video: https://youtu.be/iVfpX9BpBRo
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