A Probabilistic Programming Idiom for Active Knowledge Search
This addresses the problem of efficient environment exploration for robotics applications, but appears incremental as it applies an existing programming paradigm to a specific domain.
The paper tackles the problem of acquiring new knowledge about an environment by deriving and implementing a probabilistic programming idiom for active knowledge search, demonstrating it through active mapping and robot exploration with evaluation on the HouseExpo dataset.
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.