Learning grasp stability based on tactile data and HMMs

Yasemin Bekiroglu*, Danica Kragic, Ville Kyrki

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

39 Sitaatiot (Scopus)

Abstrakti

In this paper, the problem of learning grasp stability in robotic object grasping based on tactile measurements is studied. Although grasp stability modeling and estimation has been studied for a long time, there are few robots today able of demonstrating extensive grasping skills. The main contribution of the work presented here is an investigation of probabilistic modeling for inferring grasp stability based on learning from examples. The main objective is classification of a grasp as stable or unstable before applying further actions on it, e.g. lifting. The problem cannot be solved by visual sensing which is typically used to execute an initial robot hand positioning with respect to the object. The output of the classification system can trigger a regrasping step if an unstable grasp is identified. An off-line learning process is implemented and used for reasoning about grasp stability for a three-fingered robotic hand using Hidden Markov models. To evaluate the proposed method, experiments are performed both in simulation and on a real robot system.

AlkuperäiskieliEnglanti
OtsikkoProceedings - IEEE International Workshop on Robot and Human Interactive Communication
Sivut132-137
Sivumäärä6
DOI - pysyväislinkit
TilaJulkaistu - 2010
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE International Symposium on Robot and Human Interactive Communication - Viareggio, Italia
Kesto: 12 syyskuuta 201015 syyskuuta 2010
Konferenssinumero: 19

Conference

ConferenceIEEE International Symposium on Robot and Human Interactive Communication
LyhennettäRO-MAN
Maa/AlueItalia
KaupunkiViareggio
Ajanjakso12/09/201015/09/2010

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