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From Multi-Modal Property Dataset to Robot-Centric Conceptual Knowledge About Household Objects.

Madhura Thosar1, Christian A Mueller2, Georg Jäger3

  • 1Faculty of Computer Science, Institute for Intelligent Cooperating Systems, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.

Frontiers in Robotics and AI
|May 3, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a robot-centric framework for estimating object properties and generating conceptual knowledge. This enables robots to understand and use objects from their own perspective, improving tool substitution capabilities.

Keywords:
conceptual knowledgeknowledge acquisitionmulti-modal datasetrobot-centric knowledgesubstitute selection

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Conceptual knowledge of objects is crucial for goal-directed interaction and tool use in both humans and animals.
  • Current robotics approaches often rely on human-defined knowledge, which doesn't account for robot-specific perception and action capabilities.
  • A robot-centric approach, grounded in the robot's own perceptual experiences, is needed for developing adaptable object understanding.

Purpose of the Study:

  • To propose an extensible framework for estimating physical and functional properties of objects from a robot's perspective.
  • To generate robot-centric conceptual knowledge by transforming quantitative property data into symbolic representations.
  • To evaluate the framework's effectiveness and the utility of the generated conceptual knowledge in a tool substitution task.

Main Methods:

  • Developed an extensible property estimation framework to quantify physical (e.g., rigidity, weight) and functional (e.g., containment, support) properties of objects.
  • Employed unsupervised clustering to convert numerical property data into symbols.
  • Utilized Bivariate Joint Frequency Distributions and Sample Proportion for generating robot-centric conceptual knowledge from these symbols.
  • Created a dataset (RoCS) of 110 household objects with 6 physical and 4 functional properties.

Main Results:

  • Successfully implemented a framework for estimating object properties and generating robot-centric conceptual knowledge.
  • The Robot-Centric dataSet (RoCS) was created, containing quantitative property measurements and derived conceptual knowledge.
  • Preliminary evaluations demonstrated the framework's property estimation capabilities and the semantic relevance of the properties.
  • The generated conceptual knowledge proved useful in a tool substitution scenario.

Conclusions:

  • The proposed framework enables robots to acquire object conceptual knowledge from their own perspective, moving beyond human-centric definitions.
  • This robot-centric conceptual knowledge is essential for advanced robotic applications like intelligent tool substitution.
  • The RoCS dataset provides a valuable resource for future research in robot object understanding and knowledge representation.