Domain Optimization for Hierarchical Planning based on Set-Theory

Bernd Kast, Vincent Dietrich, Sebastian Albrecht, Wendelin Feiten, Jianwei Zhang


The design of planning domains for autonomous systems is a hard task, especially when different parties are involved. We present a domain optimization algorithm for hierarchical planners that uses a set-based formulation. Due to an automatic alignment we can compose models from different sources to a larger domain for efficient planning. The combination of domain optimization and hierarchical planning can handle large scale domains very efficiently. Our algorithm reduces the effects of the non-optimality that comes with the hierarchical approach. We demonstrate the scalability with a task and motion planning problem. In the scenario of a robotic assembly with up to 62 parts and plan lengths of over 1000 steps the planning times are kept within 15 minutes. We show the execution of our plans on a real-world dual-robot setup.


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