Abstracting Noisy Robot Programs
This work addresses the challenge of handling noisy actuators and sensors in robotics, providing a method to make probabilistic problems more manageable and explainable, though it appears incremental as it extends existing abstraction frameworks to probabilistic domains.
The paper tackles the problem of abstracting noisy robot programs by defining a bisimulation notion that allows probabilistic systems to be represented by non-stochastic theories, resulting in abstract Golog programs that simplify implementation and enable non-stochastic reasoning methods.
Abstraction is a commonly used process to represent some low-level system by a more coarse specification with the goal to omit unnecessary details while preserving important aspects. While recent work on abstraction in the situation calculus has focused on non-probabilistic domains, we describe an approach to abstraction of probabilistic and dynamic systems. Based on a variant of the situation calculus with probabilistic belief, we define a notion of bisimulation that allows to abstract a detailed probabilistic basic action theory with noisy actuators and sensors by a possibly non-stochastic basic action theory. By doing so, we obtain abstract Golog programs that omit unnecessary details and which can be translated back to a detailed program for actual execution. This simplifies the implementation of noisy robot programs, opens up the possibility of using non-stochastic reasoning methods (e.g., planning) on probabilistic problems, and provides domain descriptions that are more easily understandable and explainable.