Capturing Frame-Like Object Descriptors in Human Augmented Mapping

Mohamadreza Faridghasemnia*, Andrea Vanzo, Daniele Nardi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The model of an environment plays a crucial role in autonomous mobile robots, by providing them with the necessary task-relevant information. As robots become more intelligent, they need a richer and more expressive environment model. This model is a map that contains a structured description of the environment that can be used as the robot’s knowledge for several tasks, such as planning and reasoning. In this work, we propose a framework that allows to capture important environment descriptors, such as functionality and ownership of the robot’s surrounding objects, through verbal interaction. Specifically, we propose a corpus of verbal descriptions annotated with frame-like structures. We use the proposed dataset to train two multi-task neural architectures. We compare the two architectures through an experimental evaluation, discussing the design choices. Finally, we describe the creation of a simple interactive interface with our system, implemented through the trained model. The novelties of this work are: (i) the definition of a new problem, i.e., addressing different object descriptors, that plays a crucial role for the robot’s tasks accomplishment; (ii) a specialized corpus to support the creation of rich Semantic Maps; (iii) the design of different neural architectures, and their experimental evaluation over the proposed dataset; (iv) a simple interface for the actual usage of the proposed resources.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence
Subtitle of host publicationAI*IA 2019
EditorsMario Alviano, Gianluigi Greco, Francesco Scarcello
Number of pages13
ISBN (Electronic)9783030351663
ISBN (Print)9783030351656
Publication statusPublished - 2019
Event18th International Conference of the Italian Association for Artificial Intelligence 2019 - Rende, Italy
Duration: 19 Nov 201922 Nov 2019

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th International Conference of the Italian Association for Artificial Intelligence 2019
Abbreviated titleAI*IA 2019


  • Corpus annotator
  • Human robot interaction
  • Natural Language understanding
  • Neural networks
  • Semantic mapping
  • Semantic mapping corpus

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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