# Abstracting Noisy Robot Programs

arXiv: https://arxiv.org/abs/2204.03536 · HTML: https://mlsanity.com/paper/2204.03536/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 5 of 5)

- [Causal Consistency of Structural Equation Models](https://mlsanity.com/paper/1707.00819/) (2017)
- [Abstracting Causal Models](https://mlsanity.com/paper/1812.03789/) (2018)
- [REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics](https://mlsanity.com/paper/1508.03891/) (2015)
- [Omission-based Abstraction for Answer Set Programs](https://mlsanity.com/paper/2004.01410/) (2020)
- [Situation Calculus for Synthesis of Manufacturing Controllers](https://mlsanity.com/paper/1807.04561/) (2018)

## Built on by (most cited first, 3 of 3)

- [Abstracting Situation Calculus Action Theories](https://mlsanity.com/paper/2410.14712/) (2024)
- [Using Abstraction for Interpretable Robot Programs in Stochastic Domains](https://mlsanity.com/paper/2207.12763/) (2022)
- [An Algebraic Framework for Hierarchical Probabilistic Abstraction](https://mlsanity.com/paper/2502.21216/) (2025)

## Abstract (arXiv)

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.