Planning and Explanations with a Learned Spatial Model

Authors Susan L. Epstein, Raj Korpan

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Author Details

Susan L. Epstein
  • Department of Computer Science, Hunter College and The Graduate Center of , The City University of New York, New York, New York 10065, USA
Raj Korpan
  • Department of Computer Science, The Graduate Center of The City University of New York, New York, New York 10016, USA


The authors thank Anoop Aroor for his thoughtful comments, Gil Dekel and Sarah Mathew for their pioneering work on doors and hallways, and the anonymous referees for their many constructive suggestions.

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Susan L. Epstein and Raj Korpan. Planning and Explanations with a Learned Spatial Model. In 14th International Conference on Spatial Information Theory (COSIT 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 142, pp. 22:1-22:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


This paper reports on a robot controller that learns and applies a cognitively-based spatial model as it travels in challenging, real-world indoor spaces. The model not only describes indoor space, but also supports robust, model-based planning. Together with the spatial model, the controller’s reasoning framework allows it to explain and defend its decisions in accessible natural language. The novel contributions of this paper are an enhanced cognitive spatial model that facilitates successful reasoning and planning, and the ability to explain navigation choices for a complex environment. Empirical evidence is provided by simulation of a commercial robot in a large, complex, realistic world.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Artificial intelligence
  • Computing methodologies → Machine learning
  • Computing methodologies → Modeling and simulation
  • navigation
  • planning
  • learning
  • explanation
  • spatial model
  • heuristics


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