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An Integrated Planning and Learning Framework for Human-Robot Interaction

Abstract : Assistive robot systems require a robot to interact closely with people and to perform joint human-robot tasks. Interaction with humans comes with additional challenges to those in other real-world scenarios. Robot plans must be especially flexible and take into account human abilities and preferences. For providing this level of flexibility, we propose a framework for trans-formational reactive planning that includes the capability to learn models of the human during plan execution. We show how this framework can be extended to the special requirements of human-robot interaction.
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Contributor : Alexandra Kirsch <>
Submitted on : Wednesday, November 30, 2016 - 11:24:31 AM
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  • HAL Id : hal-01405650, version 1


Alexandra Kirsch, Thibault Kruse, Lorenz Mösenlechner. An Integrated Planning and Learning Framework for Human-Robot Interaction. 4th Workshop on Planning and Plan Execution for Real-World Systems, 2009, Thessaloniki, Greece. ⟨hal-01405650⟩



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