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Assisted teleoperation in changing environments with a mixture of virtual guides

Ewerton, Marco ; Arenz, Oleg ; Peters, Jan (2022)
Assisted teleoperation in changing environments with a mixture of virtual guides.
In: Advanced Robotics, 34 (18)
doi: 10.26083/tuprints-00023003
Article, Secondary publication, Postprint

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Item Type: Article
Type of entry: Secondary publication
Title: Assisted teleoperation in changing environments with a mixture of virtual guides
Language: English
Date: 2022
Place of Publication: Darmstadt
Publisher: Taylor & Francis
Journal or Publication Title: Advanced Robotics
Volume of the journal: 34
Issue Number: 18
Collation: 19 Seiten
DOI: 10.26083/tuprints-00023003
Corresponding Links:
Origin: Secondary publication service

Haptic guidance is a powerful technique to combine the strengths of humans and autonomous systems for teleoperation. The autonomous system can provide haptic cues to enable the operator to perform precise movements; the operator can interfere with the plan of the autonomous system leveraging his/her superior cognitive capabilities. However, providing haptic cues such that the individual strengths are not impaired is challenging because low forces provide little guidance, whereas strong forces can hinder the operator in realizing his/her plan. Based on variational inference, we learn a Gaussian mixture model (GMM) over trajectories to accomplish a given task. The learned GMM is used to construct a potential field which determines the haptic cues. The potential field smoothly changes during teleoperation based on our updated belief over the plans and their respective phases. Furthermore, new plans are learned online when the operator does not follow any of the proposed plans or after changes in the environment. User studies confirm that our framework helps users perform teleoperation tasks more accurately than without haptic cues and, in some cases, faster. Moreover, we demonstrate the use of our framework to help a subject teleoperate a 7 DoF manipulator in a pick-and-place task.

Uncontrolled Keywords: teleoperation; policy search; variational inference; movement primitives; Gaussian mixture models
Status: Postprint
URN: urn:nbn:de:tuda-tuprints-230039
Additional Information:

Supplement (video): https://doi.org/10.6084/m9.figshare.12852846.v1

Classification DDC: 000 Generalities, computers, information > 004 Computer science
Divisions: 20 Department of Computer Science > Intelligent Autonomous Systems
Date Deposited: 14 Dec 2022 13:48
Last Modified: 11 Jan 2023 14:23
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/23003
PPN: 503521302
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