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The development and uptake of robotic technologies, outside the research community, has been hindered by the fact that robotic systems are notably lacking in flexibility. Introducing humans in robot teams promises to improve their flexibility. However, the major underlying difficulty in the development of human-robot teams is the inability of robots to emulate important cognitive capabilities of human beings due to the lack of approaches to generate and effectively abstract salient semantic aspects of information and big data sets. In this paper we develop a general framework for information abstraction that allows robots to obtain high level descriptions of their perceptions. These descriptions are represented using a formal predicate logic that emulates natural language structures, facilitating human understanding while it remains easy to interpret by robots. In addition, the proposed formal logic constitutes a precisiation language that generalizes Zadeh’s Precisiated Natural Language, providing new tools for the computation with perceptions.

Hierarchical Qualitative Descriptions of Perceptions for Robotic Environments