Using learning from demonstration to generate real-time guidance for haptic shared control

C. J. Perez-Del-Pulgar, Jan Smisek, V. F. Munoz, Andre Schiele

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

5 Citations (Scopus)

Abstract

This paper introduces a new Learning from Demonstration (LfD)-based method that makes usage of robot effector forces and torques recorded during expert demonstrations, to generate force-based haptic guidance reference trajectories on-line, that are intended to be used during haptic shared control for additional operator 'guidance'. Derived haptic guidance trajectories are superimposed to master-device inputs and feedback forces within a bilateral control experiment, to assist an operator by the guidance during peg-in-hole insertion. We show that 96 peg-in-hole expert demonstrations were sufficient to obtain a good model of the task, which was used on-line to generate haptic guidance trajectories in real-time with a 1kHz sampling rate.

Original languageEnglish
Title of host publicationProceedings 2016 IEEE International Conference on Systems, Man, and Cybernetics - SMC 2016
Place of PublicationPiscataway, NJ, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages3205-3210
ISBN (Electronic)9781509018970
DOIs
Publication statusPublished - 2016
Event2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary
Duration: 9 Oct 201612 Oct 2016

Conference

Conference2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
CountryHungary
CityBudapest
Period9/10/1612/10/16

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