  This work presents a framework for automatically extracting physical object
properties, such as material composition, mass, volume, and stiffness, through
robot manipulation and a database of object measurements. The framework
involves exploratory action selection to maximize learning about objects on a
table. A Bayesian network models conditional dependencies between object
properties, incorporating prior probability distributions and uncertainty
associated with measurement actions. The algorithm selects optimal exploratory
actions based on expected information gain and updates object properties
through Bayesian inference. Experimental evaluation demonstrates effective
action selection compared to a baseline and correct termination of the
experiments if there is nothing more to be learned. The algorithm proved to
behave intelligently when presented with trick objects with material properties
in conflict with their appearance. The robot pipeline integrates with a logging
module and an online database of objects, containing over 24,000 measurements
of 63 objects with different grippers. All code and data are publicly
available, facilitating automatic digitization of objects and their physical
properties through exploratory manipulations.
