Segmenting and Sequencing of Compliant Motions

Tesfamichael Hagos, Markku Suomalainen, Ville Kyrki

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

5 Citations (Scopus)

Abstract

This paper proposes an approach for segmenting a task consisting of compliant motions into phases, learning a primitive for each segmented phase of the task, and reproducing the task by sequencing primitives online based on the learned model. As compliant motions can "probe'' the environment, using the interaction between the robot and the environment to detect phase transitions can make the transitions less prone to positional errors. This intuition leads us to model a task with a non-homogeneous Hidden Markov Model (HMM), wherein hidden phase transition probabilities depend on the interaction with the environment (wrench measured by an F/T sensor). Expectation-maximization algorithm is employed in estimating the parameters of the HMM model. During reproduction, the phase changes of a task are detected online using the forward algorithm, with the parameters learned from demonstrations. Cartesian impedance controller parameters are learned from the demonstrations to reproduce each phase of the task. The proposed approach is studied with a KUKA LWR4+ arm in two setups. Experiments show that the method can successfully segment and reproduce a task consisting of compliant motions with one or more demonstrations, even when demonstrations do not have the same starting position and external forces occur from different directions. Finally, we demonstrate that the method can also handle rotational motions.
Original languageEnglish
Title of host publicationProceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
Place of PublicationUnited States
PublisherIEEE
Pages6057-6064
Number of pages8
ISBN (Electronic)978-1-5386-8094-0
ISBN (Print)978-1-5386-8095-7
DOIs
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventIEEE/RSJ International Conference on Intelligent Robots and Systems - Madrid Municipal Conference Centre (MMCC), Madrid, Spain
Duration: 1 Oct 20185 Oct 2018
https://www.iros2018.org/

Publication series

NameProceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherIEEE
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS
CountrySpain
CityMadrid
Period01/10/201805/10/2018
Internet address

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