Classical Planning

Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations
We present a framework for the automatic encoding and repair of high-level tasks. Given a set of skills a robot can perform, our …
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Learning Abstract and Transferable Representations for Planning
We are concerned with the question of how an agent can acquire its own representations from sensory data. We restrict our focus to …
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Autonomous Learning of Object-Centric Abstractions for High-Level Planning
We propose a method for autonomously learning an object-centric representation of a continuous and high-dimensional environment that is …
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Learning Portable Representations for High-Level Planning
We present a framework for autonomously learning a portable representation that describes a collection of low-level continuous …
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Learning Object-Centric Representations for High-Level Planning in Minecraft
We propose a method for autonomously learning an object-centric representation of a highdimensional environment that is suitable for …
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Learning to Plan with Portable Symbols
We present a framework for autonomously learning a portable symbolic representation that describes a collection of low-level continuous …
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