  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.
