From Multi-modal Property Dataset to Robot-centric Conceptual Knowledge About Household Objects

TL;DR

This paper presents a framework for generating robot-centric conceptual knowledge of household objects based on physical and functional properties, using multi-modal data and unsupervised clustering, to improve robot decision-making and tool substitution.

Contribution

It introduces a novel approach to create robot-centric conceptual knowledge from multi-modal property data, including property extraction and unsupervised clustering, tailored for robotic applications.

Findings

Successfully extracted properties from 110 household objects

Generated meaningful robot-centric symbols and concepts

Demonstrated usefulness in tool substitution tasks

Abstract

Tool-use applications in robotics require conceptual knowledge about objects for informed decision making and object interactions. State-of-the-art methods employ hand-crafted symbolic knowledge which is defined from a human perspective and grounded into sensory data afterwards. However, due to different sensing and acting capabilities of robots, their conceptual understanding of objects must be generated from a robot's perspective entirely, which asks for robot-centric conceptual knowledge about objects. With this goal in mind, this article motivates that such knowledge should be based on physical and functional properties of objects. Consequently, a selection of ten properties is defined and corresponding extraction methods are proposed. This multi-modal property extraction forms the basis on which our second contribution, a robot-centric knowledge generation is build on. It employs unsupervised clustering methods to transform numerical property data into symbols, and Bivariate Joint Frequency Distributions and Sample Proportion to generate conceptual knowledge about objects using the robot-centric symbols. A preliminary implementation of the proposed framework is employed to acquire a dataset comprising physical and functional property data of 110 houshold objects. This Robot-Centric dataSet (RoCS) is used to evaluate the framework regarding the property extraction methods, the semantics of the considered properties within the dataset and its usefulness in real-world applications such as tool substitution.