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Planning in Action Language BC while Learning Action Costs for Mobile Robots

Authors

Piyush Khandelwal,Fangkai Yang,Matteo Leonetti,Vladimir Lifschitz,Peter Stone

The University of Texas at Austin,The University of Texas at Austin,The University of Texas at Austin,The University of Texas at Austin,The University of Texas at Austin


Proceedings:

Book One

Volume

Issue:

Proceedings of the International Conference on Automated Planning and Scheduling, 24

Track:

Robotics Special Track Full Papers

Downloads:

Download PDF

Abstract:

The action language BC provides an elegant way of formalizing dynamic domains which involve indirect effects of actions and recursively defined fluents. In complex robot task planning domains, it may be necessary for robots to plan with incomplete information, and reason about indirect or recursive action effects. In this paper, we demonstrate how BC can be used for robot task planning to solve these issues. Additionally, action costs are incorporated with planning to produce optimal plans, and we estimate these costs from experience making planning adaptive. This paper presents the first application of BC on a real robot in a realistic domain, which involves human-robot interaction for knowledge acquisition, optimal plan generation to minimize navigation time, and learning for adaptive planning.

DOI:

10.1609/icaps.v24i1.13671


ICAPS

Proceedings of the International Conference on Automated Planning and Scheduling, 24