Robot task planning and explanation in open and uncertain worlds

Hanheide, Marc, Göbelbecker, Moritz, Horn, Graham S. , Pronobis, Andrzej, Sjöö, Kristoffer, Aydemir, Alper, Jensfelt, Patric, Gretton, Charles, Dearden, Richard, Janicek, Miroslav, Zender, Hendrik, Kruijff, Geert-Jan, Hawes, Nick and Wyatt, Jeremy L. (2017) Robot task planning and explanation in open and uncertain worlds. Artificial Intelligence, 247 . pp. 119-150. ISSN 0004-3702

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A long-standing goal of AI is to enable robots to plan in the face of uncertain and incomplete information, and to handle task failure intelligently. This paper shows how to achieve this. There are two central ideas. The first idea is to organize the robot's knowledge into three layers: instance knowledge at the bottom, commonsense knowledge above that, and diagnostic knowledge on top. Knowledge in a layer above can be used to modify knowledge in the layer(s) below. The second idea is that the robot should represent not just how its actions change the world, but also what it knows or believes. There are two types of knowledge effects the robot's actions can have: epistemic effects (I believe X because I saw it) and assumptions (I'll assume X to be true). By combining the knowledge layers with the models of knowledge effects, we can simultaneously solve several problems in robotics: (i) task planning and execution under uncertainty; (ii) task planning and execution in open worlds; (iii) explaining task failure; (iv) verifying those explanations. The paper describes how the ideas are implemented in a three-layer architecture on a mobile robot platform. The robot implementation was evaluated in five different experiments on object search, mapping, and room categorization.

Keywords:Artificial intelligence
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
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ID Code:18592
Deposited On:10 Sep 2015 09:16

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