Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Large-scale supervised learning of the grasp robustness of surface patch pairs
KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.ORCID-id: 0000-0003-1114-6040
KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS. KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL.ORCID-id: 0000-0003-2965-2953
Vise andre og tillknytning
2017 (engelsk)Inngår i: 2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2016, Institute of Electrical and Electronics Engineers Inc. , 2017, s. 216-223Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The robustness of a parallel-jaw grasp can be estimated by Monte Carlo sampling of perturbations in pose and friction but this is not computationally efficient. As an alternative, we consider fast methods using large-scale supervised learning, where the input is a description of a local surface patch at each of two contact points. We train and test with disjoint subsets of a corpus of 1.66 million grasps where robustness is estimated by Monte Carlo sampling using Dex-Net 1.0. We use the BIDMach machine learning toolkit to compare the performance of two supervised learning methods: Random Forests and Deep Learning. We find that both of these methods learn to estimate grasp robustness fairly reliably in terms of Mean Absolute Error (MAE) and ROC Area Under Curve (AUC) on a held-out test set. Speedups over Monte Carlo sampling are approximately 7500x for Random Forests and 1500x for Deep Learning.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers Inc. , 2017. s. 216-223
Emneord [en]
Decision trees, Deep learning, Learning systems, Robot programming, Robots, Supervised learning, Computationally efficient, Disjoint subsets, Local surfaces, Mean absolute error, Monte Carlo sampling, Random forests, Supervised learning methods, Surface patches, Monte Carlo methods
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-207997DOI: 10.1109/SIMPAR.2016.7862399ISI: 000405933700032Scopus ID: 2-s2.0-85015928918ISBN: 9781509046164 (tryckt)OAI: oai:DiVA.org:kth-207997DiVA, id: diva2:1106852
Konferanse
2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2016, 13 December 2016 through 16 December 2016
Merknad

QC 20170608

Tilgjengelig fra: 2017-06-08 Laget: 2017-06-08 Sist oppdatert: 2017-11-10bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstScopushttp://simpar2016.org/

Personposter BETA

Pokorny, Florian T.Kragic, Danica

Søk i DiVA

Av forfatter/redaktør
Pokorny, Florian T.Kragic, Danica
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric

doi
isbn
urn-nbn
Totalt: 34 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf