Experience-based torque estimation for an industrial robot

Erik Berger, Steve Grehl, David Vogt, Bernhard Jung, Hani Ben Amor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Robotic manipulation tasks often require the control of forces and torques exerted on external objects. This paper presents a machine learning approach for estimating forces when no force sensors are present on the robot platform. In the training phase, the robot executes the desired manipulation tasks under controlled conditions with systematically varied parameter sets. All internal sensor data, in the presented case from more than 100 sensors, as well as the force exerted by the robot are recorded. Using Transfer Entropy, a statistical model is learned that identifies the subset of sensors relevant for torque estimation in the given task. At runtime, the model is used to accurately estimate the torques exerted during manipulations of the demonstrated kind. The feasibility of the approach is shown in a setting where a robotic manipulator operates a torque wrench to fasten a screw nut. Torque estimates with an accuracy of well below ±1Nm are achieved. A strength of the presented model is that no prior knowledge of the robot's kinematics, mass distribution or sensor instrumentation is required.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Robotics and Automation, ICRA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages144-149
Number of pages6
Volume2016-June
ISBN (Electronic)9781467380263
DOIs
StatePublished - Jun 8 2016
Event2016 IEEE International Conference on Robotics and Automation, ICRA 2016 - Stockholm, Sweden
Duration: May 16 2016May 21 2016

Other

Other2016 IEEE International Conference on Robotics and Automation, ICRA 2016
CountrySweden
CityStockholm
Period5/16/165/21/16

Fingerprint

Industrial robots
Torque
Robots
Sensors
Robotics
Nuts (fasteners)
Hand tools
Set theory
Manipulators
Learning systems
Kinematics
Entropy

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Berger, E., Grehl, S., Vogt, D., Jung, B., & Ben Amor, H. (2016). Experience-based torque estimation for an industrial robot. In 2016 IEEE International Conference on Robotics and Automation, ICRA 2016 (Vol. 2016-June, pp. 144-149). [7487127] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA.2016.7487127

Experience-based torque estimation for an industrial robot. / Berger, Erik; Grehl, Steve; Vogt, David; Jung, Bernhard; Ben Amor, Hani.

2016 IEEE International Conference on Robotics and Automation, ICRA 2016. Vol. 2016-June Institute of Electrical and Electronics Engineers Inc., 2016. p. 144-149 7487127.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Berger, E, Grehl, S, Vogt, D, Jung, B & Ben Amor, H 2016, Experience-based torque estimation for an industrial robot. in 2016 IEEE International Conference on Robotics and Automation, ICRA 2016. vol. 2016-June, 7487127, Institute of Electrical and Electronics Engineers Inc., pp. 144-149, 2016 IEEE International Conference on Robotics and Automation, ICRA 2016, Stockholm, Sweden, 5/16/16. https://doi.org/10.1109/ICRA.2016.7487127
Berger E, Grehl S, Vogt D, Jung B, Ben Amor H. Experience-based torque estimation for an industrial robot. In 2016 IEEE International Conference on Robotics and Automation, ICRA 2016. Vol. 2016-June. Institute of Electrical and Electronics Engineers Inc. 2016. p. 144-149. 7487127 https://doi.org/10.1109/ICRA.2016.7487127
Berger, Erik ; Grehl, Steve ; Vogt, David ; Jung, Bernhard ; Ben Amor, Hani. / Experience-based torque estimation for an industrial robot. 2016 IEEE International Conference on Robotics and Automation, ICRA 2016. Vol. 2016-June Institute of Electrical and Electronics Engineers Inc., 2016. pp. 144-149
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