Title:
Using a Shared Tablet Workspace for Interactive Demonstrations during Human-Robot Learning Scenarios
Using a Shared Tablet Workspace for Interactive Demonstrations during Human-Robot Learning Scenarios
dc.contributor.author | Park, Hae Won | |
dc.contributor.author | Coogle, Richard A. | |
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 | 2015-09-04T15:58:07Z | |
dc.date.available | 2015-09-04T15:58:07Z | |
dc.date.issued | 2014 | |
dc.description | © 2014 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 | 2014 IEEE International Conference on Robotics and Automation (ICRA 2014), May 31 2014-June 7 2014, Hong Kong, China. | |
dc.description | DOI: 10.1109/ICRA.2014.6907248 | |
dc.description.abstract | One of the key elements for building a long-term robotic companion is incorporating the ability for a robot to continuously learn and engage in new tasks. Utilizing a defined workspace that provides various shared content between human and robot could assist in this learning process. Here, we propose integrating a touchscreen tablet and a robot learner for engaging the user during human-robot interaction scenarios. The robot learner’s domain-independent core reasoner follows the structure of instance-based learning which addresses the issues of acquiring knowledge, encoding cases, and learning a retrieval metric. The system utilizes demonstrations provided by the user to auto-populate the knowledge base through natural interaction methods, encodes cases based on the feature structure provided by the user, and uses an adaptive-weighting technique to design a retrieval metric with linear regression in the feature-distance space. Through a tablet environment, the user teaches a task to the robot in a shared workspace and intuitively monitors the robot’s behavior and progress in real time. In this setting, the user is able to interrupt the robot and provide necessary demonstrations at the moment learning is taking place, thus providing a means to continuously engage both the participant and the robot in the learning cycle. | en_US |
dc.embargo.terms | null | en_US |
dc.identifier.citation | Park, H. W.; Coogle, R. A.; & Howard, A. (2014). Using a shared tablet workspace for interactive demonstrations during human-robot learning scenarios". 2014 IEEE International Conference on Robotics and Automation (ICRA 2014), May 31 2014-June 7 2014, pp. 2713-2719. | en_US |
dc.identifier.uri | http://hdl.handle.net/1853/53806 | |
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 | Shared workspace | en_US |
dc.subject | Touchscreen tablet | en_US |
dc.title | Using a Shared Tablet Workspace for Interactive Demonstrations during Human-Robot Learning Scenarios | 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) | |
relation.isAuthorOfPublication | 6d77e175-105c-4b0b-9548-31f20e60e20a | |
relation.isOrgUnitOfPublication | 88639fad-d3ae-4867-9e7a-7c9e6d2ecc7c | |
relation.isOrgUnitOfPublication | 66259949-abfd-45c2-9dcc-5a6f2c013bcf |
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