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

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.Comment: Accepted to the IEEE International Conference on Robotics and Automation (ICRA'23), London, UK, May 29 - June 2, 202

Real-time identification of robot payload using a multirate quaternion-based kalman filter and recursive total least-squares

The paper describes an estimation and identification procedure that allows to reconstruct the inertial parameters of a rigid load attached to the end-effector of an industrial manipulator. In particular, the proposed method adopts a multirate quaternion-based Kalman filter, fusing measurements obtained from robot kinematics and inertial sensors at possibly different sampling frequencies, to estimate linear accelerations and angular velocities/accelerations of the load. Then, a recursive total least-squares (RTLS) process is executed to identify the load parameters. Both steps of the estimation and identification procedure are performed in real-time, without the need for offline post-processing of measured data