A Probabilistic Programming Idiom for Active Knowledge Search

TL;DR

This paper introduces a probabilistic programming approach for active knowledge acquisition, demonstrated through robot exploration and mapping, validated with extensive simulations using the HouseExpo dataset.

Contribution

It presents a novel probabilistic programming idiom tailored for active knowledge search, applied to robot exploration and mapping tasks.

Findings

Effective active mapping algorithm demonstrated in simulation

Validates the utility of the probabilistic programming idiom

Shows promising results in environment exploration

Abstract

In this paper, we derive and implement a probabilistic programming idiom for the problem of acquiring new knowledge about an environment. The idiom is implemented utilizing a modern probabilistic programming language. We demonstrate the utility of this idiom by implementing an algorithm for the specific problem of active mapping and robot exploration. Finally, we evaluate the functionality of the implementation through an extensive simulation study utilizing the HouseExpo dataset.