Estimating An Object’s Inertial Parameters By Robotic Pushing: A Data-Driven Approach
Nikos Mavrakis, Amir M. Ghalamzan E., Rustam Stolkin
Abstract
Estimating the inertial properties of an object can make robotic manipulations more efficient, especially in extreme environments. This paper presents a novel method of estimating the 2D inertial parameters of an object, by having a robot applying a push on it. We draw inspiration from previous analyses on quasi-static pushing mechanics, and introduce a data-driven model that can accurately represent these mechanics and provide a prediction for the object's inertial parameters. We evaluate the model with two datasets. For the first dataset, we set up a V-REP simulation of seven robots pushing objects with large range of inertial parameters, acquiring 48000 pushes in total. For the second dataset, we use the object pushes from the MIT M-Cube lab pushing dataset. We extract features from force, moment and velocity measurements of the pushes, and train a Multi-Output Regression Random Forest. The experimental results show that we can accurately predict the 2D inertial parameters from a single push, and that our method retains this robust performance under various surface types.
BibTeX
@inproceedings{iros2020_estimatinganobje,
title = {Estimating An Object’s Inertial Parameters By Robotic Pushing: A Data-Driven Approach},
author = {Nikos Mavrakis and Amir M. Ghalamzan E. and Rustam Stolkin},
booktitle = {IROS 2020},
year = {2020}
}