Learning Efficient Abstract Planning Models that Choose What to Predict

Understand

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.