  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
