  Meta-planning, or learning to guide planning from experience, is a promising
approach to improving the computational cost of planning. A general
meta-planning strategy is to learn to impose constraints on the states
considered and actions taken by the agent. We observe that (1) imposing a
constraint can induce context-specific independences that render some aspects
of the domain irrelevant, and (2) an agent can take advantage of this fact by
imposing constraints on its own behavior. These observations lead us to propose
the context-specific abstract Markov decision process (CAMP), an abstraction of
a factored MDP that affords efficient planning. We then describe how to learn
constraints to impose so the CAMP optimizes a trade-off between rewards and
computational cost. Our experiments consider five planners across four domains,
including robotic navigation among movable obstacles (NAMO), robotic task and
motion planning for sequential manipulation, and classical planning. We find
planning with learned CAMPs to consistently outperform baselines, including
Stilman's NAMO-specific algorithm. Video: https://youtu.be/wTXt6djcAd4 Code:
https://git.io/JTnf6

Machine Learning;Artificial Intelligence;Robotics