  To operate safely and efficiently alongside human workers, collaborative
robots (cobots) require the ability to quickly understand the dynamics of
manipulated objects. However, traditional methods for estimating the full set
of inertial parameters rely on motions that are necessarily fast and unsafe (to
achieve a sufficient signal-to-noise ratio). In this work, we take an
alternative approach: by combining visual and force-torque measurements, we
develop an inertial parameter identification algorithm that requires slow or
'stop-and-go' motions only, and hence is ideally tailored for use around
humans. Our technique, called Homogeneous Part Segmentation (HPS), leverages
the observation that man-made objects are often composed of distinct,
homogeneous parts. We combine a surface-based point clustering method with a
volumetric shape segmentation algorithm to quickly produce a part-level
segmentation of a manipulated object; the segmented representation is then used
by HPS to accurately estimate the object's inertial parameters. To benchmark
our algorithm, we create and utilize a novel dataset consisting of realistic
meshes, segmented point clouds, and inertial parameters for 20 common workshop
tools. Finally, we demonstrate the real-world performance and accuracy of HPS
by performing an intricate 'hammer balancing act' autonomously and online with
a low-cost collaborative robotic arm. Our code and dataset are open source and
freely available.
