Planning with Noisy Actions

Ignoring the noise of physical sensors and effectors has always been a crucial barrier towards the application of high-level, cognitive robotics to real robots. We present a method of solving planning problems with noisy actions. The approach builds on the Fluent Calculus as a standard first-order solution to the Frame Problem. To model noise, a formal notion of uncertainty is incorporated into the axiomatization of state update and knowledge update. The formalism provides the theoretical underpinnings of an extension of the action programming language Flux. Using constraints on real-valued intervals to encode noise, our system allows to solve planning problems for noisy sensors and effectors.

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