Title:
Retrieving Experience: Interactive Instance-based Learning Methods for Building Robot Companions

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dc.contributor.author Park, Hae Won
dc.contributor.author Howard, Ayanna M.
dc.contributor.corporatename Georgia Institute of Technology. Institute for Robotics and Intelligent Machines en_US
dc.contributor.corporatename Georgia Institute of Technology. School of Electrical and Computer Engineering en_US
dc.contributor.corporatename Georgia Institute of Technology. Human-Automation Systems Lab en_US
dc.date.accessioned 2015-09-08T17:28:08Z
dc.date.available 2015-09-08T17:28:08Z
dc.date.issued 2015-05
dc.description © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. en_US
dc.description 2015 IEEE International Conference on Robotics and Automation (ICRA 2015), 26-30 May 2015, Seattle, WA.
dc.description DOI: 10.1109/ICRA.2015.7140061
dc.description.abstract A robot companion should adapt to its user’s needs by learning to perform new tasks. In this paper, we present a robot playmate that learns and adapts to tasks chosen by the child on a touchscreen tablet. We aim to solve the task learning problem using an experience-based learning framework that stores human demonstrations as task instances. These instances are retrieved when confronted with a similar task in which the system generates predictions of task behaviors based on prior actions. In order to automate the processes of instance encoding, acquisition, and retrieval, we have developed a framework that gathers task knowledge through interaction with human teachers. This approach, further referred to as interactive instance-based learning (IIBL), utilizes limited information available to the robot to generate similarity metrics for retrieving instances. In this paper, we focus on introducing and evaluating a new hybrid IIBL framework using sensitivity analysis with artificial neural networks and discuss its advantage over methods using k-NNs and linear regression in retrieving instances. en_US
dc.embargo.terms null en_US
dc.identifier.citation Park, H. W. & Howard, A. M. (2015). "Retrieving experience: Interactive instance-based learning methods for building robot companions". 2015 IEEE International Conference on Robotics and Automation (ICRA 2015), 26-30 May 2015, pp. 6140-6145. en_US
dc.identifier.doi 10.1109/ICRA.2015.7140061
dc.identifier.uri http://hdl.handle.net/1853/53808
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.publisher.original Institute of Electrical and Electronics Engineers
dc.subject Human-robot interaction en_US
dc.subject Robot learner en_US
dc.subject Robot playmate en_US
dc.subject Shared workspace en_US
dc.subject Touchscreen tablet en_US
dc.title Retrieving Experience: Interactive Instance-based Learning Methods for Building Robot Companions en_US
dc.type Text
dc.type.genre Proceedings
dspace.entity.type Publication
local.contributor.author Howard, Ayanna M.
local.contributor.corporatename School of Civil and Environmental Engineering
local.contributor.corporatename Institute for Robotics and Intelligent Machines (IRIM)
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relation.isOrgUnitOfPublication 88639fad-d3ae-4867-9e7a-7c9e6d2ecc7c
relation.isOrgUnitOfPublication 66259949-abfd-45c2-9dcc-5a6f2c013bcf
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