Marc Hanheide, Charles Gretton, Richard Dearden, Nick Hawes, Jeremy L. Wyatt, Andrzej Pronobis, Alper Aydemir, Moritz Golbelbecker and Hendrik Zender.
Exploiting probabilistic knowledge under uncertain sensing for efficient robot behaviour.
In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI'11).
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 particular
environment. Our second contribution is a continual planning system
which is able to plan in the large problems posed by that model, by
automatically switching between decision-theoretic and classical
procedures. We evaluate our system on object search 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.
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