An Integrated Planning and Learning Framework for Human-Robot Interaction

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 transformational 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.