Exploiting Probabilistic Knowledge under Uncertain Sensing for Efficient Robot Behaviour
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Robots must perform tasks efficiently and reliably while acting under uncertainty. One way to achieve efficiency is to give the robot commonsense 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.
Twenty-Second International Joint Conference on Artificial Intelligence Proceedings
Copyright 2011 AAAI Press. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
Artificial Intelligence and Image Processing not elsewhere classified