DC Field | Value | Language |
---|---|---|
dc.contributor.author | Han, Byungkil | ko |
dc.contributor.author | Kim, Seung-Chan | ko |
dc.contributor.author | Kwon, Dong-Soo | ko |
dc.date.accessioned | 2019-01-23T06:56:43Z | - |
dc.date.available | 2019-01-23T06:56:43Z | - |
dc.date.created | 2018-12-18 | - |
dc.date.issued | 2018-11 | - |
dc.identifier.citation | IEEE ACCESS, v.6, pp.76263 - 76270 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | http://hdl.handle.net/10203/250151 | - |
dc.description.abstract | Handheld virtual reality (VR) controllers are necessary for creating immersive experiences. In this paper, we propose a gated RNN-based sequence model that estimates the joint torques of a serially linked handheld VR system interface from a sequential position input. In our previous study, we proposed a motion planning algorithm for articulated systems based on the active contour model that optimizes the positions of each joint torque based on the measured base position (6-Degrees of Freedom). Because the position-to-position scheme, which calculates the joint positions from a given base position, illustrated several limitations concerning safety (i.e. unable to handle unexpected contact with the surroundings), our current study proposes a position-to-torque generation scheme that estimates the joint torques from the measured base position sequences. To that end, we trained the sequences of joint torques and the sequence of the 6-DoF base position as a supervised learning task. To model the multivariate temporal information of the sequences, we employed a gated recurrent unit. The experimental results validate the successful generation of joint trajectory profiles. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | DeepSnake: Sequence Learning of Joint Torques Using a Gated Recurrent Neural Network | - |
dc.type | Article | - |
dc.identifier.wosid | 000454297100001 | - |
dc.identifier.scopusid | 2-s2.0-85057860064 | - |
dc.type.rims | ART | - |
dc.citation.volume | 6 | - |
dc.citation.beginningpage | 76263 | - |
dc.citation.endingpage | 76270 | - |
dc.citation.publicationname | IEEE ACCESS | - |
dc.identifier.doi | 10.1109/ACCESS.2018.2880882 | - |
dc.contributor.localauthor | Kwon, Dong-Soo | - |
dc.contributor.nonIdAuthor | Kim, Seung-Chan | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
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