Learning Symbolic Representations for Planning with Parameterized Skills

A critical capability required for generally intelligent robot behavior is the ability to sequence motor skills to reach a goal. This requires a (typically abstract) representation that supports goal-directed planning, which raises the question of how to construct such a representation. Previous work has addressed this question in the context of simple black-box motor skills, which are insufficiently flexible to support the wide range of behavior required of intelligent robots. We now extend that work to include parametrized motor skills, where a robot must both select an action to execute and also decide how to parametrize it. We show how to construct a representation suitable for planning with parametrized motor skills, and specify conditions which are sufficient to separate the selection of motor skills from the parametrization of those skills. Our method results in a simple discrete abstract representation for planning followed by a parameter selection process that operates on a fixed plan. We first demonstrate learning this representation in a virtual domain based on Angry Birds and then learn an abstract symbolic representation for a robot manipulation task.