  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
