Estimating An Object’s Inertial Parameters By Robotic Pushing: A Data-Driven Approach

Mavrakis, Nikos, Stolkin, Rustam and Ghalamzan Esfahani, Amir (2020) Estimating An Object’s Inertial Parameters By Robotic Pushing: A Data-Driven Approach. In: The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 25/10/2020, Las Vegas.

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Estimating An Object’s Inertial Parameters By Robotic Pushing: A Data-Driven Approach
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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 mechan- ics 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.

Keywords:Robotic Pushing, Mass Estimation
Subjects:H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
ID Code:42213
Deposited On:28 Oct 2020 14:20

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