Publication Type

Journal Article

Version

publishedVersion

Publication Date

9-2023

Abstract

Many robots utilize commercial force/torque sensors to identify inertial properties of unknown objects. However, such sensors can be difficult to apply to small-sized robots due to their weight, size, and cost. In this letter, we propose a learning-based approach for estimating the mass and center of mass (COM) of unknown objects without using force/torque sensors at the end effector or on the joints. In our method, a robot arm carries an unknown object as it moves through multiple discrete configurations. Measurements are collected when the robot reaches each discrete configuration and stops. A neural network then estimates joint torques from encoder discrepancies. Given multiple samples, we derive the closed-form relation between joint torques and the object’s inertial properties. Based on the derivation, the mass and COM of the object are identified by weighted least squares. In order to improve the accuracy of inferred inertial properties, an attention model is designed to generate the weights used in least squares, which indicate the relative importance for each joint. Our framework requires only encoder measurements without using any force/torque sensors, but still maintains accurate estimation capability. The proposed approach has been demonstrated on a 4-degrees-of-freedom robot arm.

Keywords

Attention mechanism, calibration and identification, representation learning

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

IEEE Robotics and Automation Letters

Volume

8

Issue

9

First Page

5283

Last Page

5290

ISSN

2377-3766

Identifier

10.1109/LRA.2023.3293723

Publisher

Institute of Electrical and Electronics Engineers

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