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A Probabilistic Programming Idiom for Active Knowledge Search

arXiv 2202.09555 ↗First posted 19 Feb 2022cs.RO cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 16 references that are in the map. First posted on arXiv 19 Feb 2022.

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References · 5

  1. 2018Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Reviewcited by 466
  2. 2018Pyro: Deep Universal Probabilistic Programmingcited by 379
  3. 2019HouseExpo: A Large-scale 2D Indoor Layout Dataset for Learning-based Algorithms on Mobile Robotscited by 23
  4. 2018Frontier Based Exploration for Autonomous Robotcited by 9
  5. 2021Toward an Idiomatic Framework for Cognitive Roboticscited by 2

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