Exploiting probabilistic knowledge under uncertain sensing for efficient robot behaviour
2011 (English)In: 22nd International Joint Conference on Artificial Intelligence, 2011Conference paper (Refereed)
Robots must perform tasks efficiently and reliably while acting under uncertainty. One way to achieve efficiency is to give the robot common-sense knowledge about the structure of the world. Reliable robot behaviour can be achieved by modelling the uncertainty in the world probabilistically. We present a robot system that combines these two approaches and demonstrate the improvements in efficiency and reliability that result. Our first contribution is a probabilistic relational model integrating common-sense knowledge about the world in general, with observations of a particularenvironment. Our second contribution is a continual planning system which isable to plan in the large problems posed by that model, by automatically switching between decision-theoretic and classical procedures. We evaluate our system on objects earch tasks in two different real-world indoor environments. By reasoning about the trade-offs between possible courses of action with different informational effects, and exploiting the cues and general structures of those environments, our robot is able to consistently demonstrate efficient and reliable goal-directed behaviour.
Place, publisher, year, edition, pages
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-34159ScopusID: 2-s2.0-84881058154OAI: oai:DiVA.org:kth-34159DiVA: diva2:419590
22nd International Joint Conference on Artificial Intelligence (IJCAI’ 11), Barcelona, Spain, July 2011
QC 201105272011-05-272011-05-272011-05-27Bibliographically approved