Abstracting Noisy Robot Programs

TL;DR

This paper introduces a method for abstracting noisy probabilistic robot programs using a variant of the situation calculus, enabling simpler, more understandable, and non-stochastic reasoning on complex probabilistic systems.

Contribution

It extends abstraction techniques in situation calculus to probabilistic and dynamic systems, allowing the simplification of noisy robot programs through bisimulation.

Findings

Abstract Golog programs omit unnecessary details.

Abstract programs can be translated back for execution.

Simplifies implementation and reasoning on probabilistic robot systems.

Abstract

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.