Transferring Human Manipulation Knowledge to Robots with Inverse Reinforcement Learning

Emil Blixt Hansen*, Rasmus Eckholdt Andersen, Steffen Madsen, Simon Bøgh

*Kontaktforfatter

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

57 Downloads (Pure)

Abstrakt

The need for adaptable models, e.g. reinforcement learning, have in recent years been more present within the industry. In this paper, we show how two versions of inverse reinforcement learning can be used to transfer task knowledge from a human expert to a robot in a dynamic environment. Moreover, a second method called Principal Component Analysis weighting is presented and discussed. The method shows potential in the use case but requires some more research.
OriginalsprogEngelsk
TitelProceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020
Antal sider5
ForlagIEEE
Publikationsdato2020
Sider933-937
Artikelnummer9025873
ISBN (Elektronisk)9781728166674
DOI
StatusUdgivet - 2020
BegivenhedIEEE/SICE International Symposium on System Integration - Hawaii Convention Center, Honolulu, USA
Varighed: 12 jan. 202015 jan. 2020

Konference

KonferenceIEEE/SICE International Symposium on System Integration
LokationHawaii Convention Center
LandUSA
ByHonolulu
Periode12/01/202015/01/2020
NavnProceedings of the 2020 IEEE/SICE International Symposium on System Integration
ISSN2474-2325

Fingeraftryk Dyk ned i forskningsemnerne om 'Transferring Human Manipulation Knowledge to Robots with Inverse Reinforcement Learning'. Sammen danner de et unikt fingeraftryk.

Citationsformater