The Sum of Its Parts: Visual Part Segmentation for Inertial Parameter Identification of Manipulated Objects

TL;DR

This paper introduces a visual part segmentation method called Homogeneous Part Segmentation (HPS) that accurately estimates inertial parameters of manipulated objects using slow motions, enhancing safety and efficiency in human-robot collaboration.

Contribution

The paper presents a novel HPS algorithm combining surface clustering and shape segmentation for inertial parameter identification with slow motions, suitable for safe human-robot interaction.

Findings

HPS achieves accurate inertial parameter estimation from slow motions.

The method is validated on a new dataset of workshop tools.

Real-world robotic experiments demonstrate practical effectiveness.

Abstract

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.