Learning Efficient Abstract Planning Models that Choose What to Predict

• URL: http://arxiv.org/abs/2208.07737v3
• Date: Tue, 5 Sep 2023 16:22:41 GMT
• Title: Learning Efficient Abstract Planning Models that Choose What to Predict
• Authors: Nishanth Kumar, Willie McClinton, Rohan Chitnis, Tom Silver, Tom\'as Lozano-P\'erez, Leslie Pack Kaelbling
• Abstract summary: We show that existing symbolic operator learning approaches fall short in many robotics domains. This is primarily because they attempt to learn operators that exactly predict all observed changes in the abstract state. We propose to learn operators that 'choose what to predict' by only modelling changes necessary for abstract planning to achieve specified goals.
• Score: 28.013014215441505
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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