  An effective approach to solving long-horizon tasks in robotics domains with
continuous state and action spaces is bilevel planning, wherein a high-level
search over an abstraction of an environment is used to guide low-level
decision-making. Recent work has shown how to enable such bilevel planning by
learning abstract models in the form of symbolic operators and neural samplers.
In this work, we show that existing symbolic operator learning approaches fall
short in many robotics domains where a robot's actions tend to cause a large
number of irrelevant changes in the abstract state. This is primarily because
they attempt to learn operators that exactly predict all observed changes in
the abstract state. To overcome this issue, we propose to learn operators that
'choose what to predict' by only modelling changes necessary for abstract
planning to achieve specified goals. Experimentally, we show that our approach
learns operators that lead to efficient planning across 10 different hybrid
robotics domains, including 4 from the challenging BEHAVIOR-100 benchmark,
while generalizing to novel initial states, goals, and objects.
