Title:
Keyframe-based Learning from Demonstration Method and Evaluation
Keyframe-based Learning from Demonstration Method and Evaluation
| dc.contributor.author | Akgun, Baris | |
| dc.contributor.author | Cakmak, Maya | |
| dc.contributor.author | Jiang, Karl | |
| dc.contributor.author | Thomaz, Andrea L. | |
| dc.contributor.corporatename | Georgia Institute of Technology. Center for Robotics and Intelligent Machines | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. School of Interactive Computing | en_US |
| dc.date.accessioned | 2012-08-31T19:39:57Z | |
| dc.date.available | 2012-08-31T19:39:57Z | |
| dc.date.issued | 2012-06 | |
| dc.description | The original publication is available at www.springerlink.com | en_US |
| dc.description | DOI 10.1007/s12369-012-0160-0 | en_US |
| dc.description.abstract | We present a framework for learning skills from novel types of demonstrations that have been shown to be desirable from a human-robot interaction perspective. Our approach –Keyframe-based Learning from Demonstration (KLfD)– takes demonstrations that consist of keyframes; a sparse set of points in the state space that produces the intended skill when visited in sequence. The conventional type of trajectory demonstrations or a hybrid of the two are also handled by KLfD through a conversion to keyframes. Our method produces a skill model that consists of an ordered set of keyframe clusters, which we call Sequential Pose Distributions (SPD). The skill is reproduced by splining between clusters. We present results from two domains: mouse gestures in 2D and scooping, pouring and placing skills on a humanoid robot. KLfD has performance similar to existing LfD techniques when applied to conventional trajectory demonstrations. Additionally, we demonstrate that KLfD may be preferable when demonstration type is suited for the skill. | en_US |
| dc.identifier.citation | B. Akgun, M. Cakmak, K. Jiang, and A.L. Thomaz, “Keyframe-based learning from demonstration,” to appear in International Journal of Social Robotics, 2012. | en_US |
| dc.identifier.issn | 1875-4791 | |
| dc.identifier.uri | http://hdl.handle.net/1853/44594 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.publisher.original | Springer | |
| dc.subject | Learning from demonstration | en_US |
| dc.subject | Kinesthetic teaching | en_US |
| dc.subject | Human-robot interaction | en_US |
| dc.subject | Humanoid robotics | en_US |
| dc.title | Keyframe-based Learning from Demonstration Method and Evaluation | en_US |
| dc.type | Text | |
| dc.type.genre | Article | |
| dc.type.genre | Pre-print | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | College of Computing | |
| local.contributor.corporatename | Socially Intelligent Machines Lab | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
| relation.isOrgUnitOfPublication | c8892b3c-8db6-4b7b-a33a-1b67f7db2021 | |
| relation.isOrgUnitOfPublication | 57e47d4b-8e04-4c68-a99e-2cb4580b4844 | |
| relation.isOrgUnitOfPublication | 66259949-abfd-45c2-9dcc-5a6f2c013bcf |
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