Title:
Haptic Classification and Recognition of Objects Using a Tactile Sensing Forearm
Haptic Classification and Recognition of Objects Using a Tactile Sensing Forearm
| dc.contributor.author | Bhattacharjee, Tapomayukh | en_US |
| dc.contributor.author | Rehg, James M. | en_US |
| dc.contributor.author | Kemp, Charles C. | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. Healthcare Robotics Lab | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. Computational Perception Lab | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. Institute for Robotics and Intelligent Machines | en_US |
| dc.date.accessioned | 2013-12-19T15:22:28Z | |
| dc.date.available | 2013-12-19T15:22:28Z | |
| dc.date.issued | 2012-10 | |
| dc.description | ©2012 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 | Presented at the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, Algarve, Portugal, October 7-12, 2012. | en_US |
| dc.description | DOI: 10.1109/IROS.2012.6386142 | en_US |
| dc.description.abstract | In this paper, we demonstrate data-driven inference of mechanical properties of objects using a tactile sensor array (skin) covering a robot's forearm. We focus on the mobility (sliding vs. fixed), compliance (soft vs. hard), and identity of objects in the environment, as this information could be useful for efficient manipulation and search. By using the large surface area of the forearm, a robot could potentially search and map a cluttered volume more efficiently, and be informed by incidental contact during other manipulation tasks. Our approach tracks a contact region on the forearm over time in order to generate time series of select features, such as the maximum force, contact area, and contact motion. We then process and reduce the dimensionality of these time series to generate a feature vector to characterize the contact. Finally, we use the k-nearest neighbor algorithm (k-NN) to classify a new feature vector based on a set of previously collected feature vectors. Our results show a high cross-validation accuracy in both classification of mechanical properties and object recognition. In addition, we analyze the effect of taxel resolution, duration of observation, feature selection, and feature scaling on the classification accuracy. | en_US |
| dc.identifier.citation | Haptic Classification and Recognition of Objects Using a Tactile Sensing Forearm, Tapomayukh Bhattacharjee, James M. Rehg, and Charles C. Kemp, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 4090-4097. | en_US |
| dc.identifier.doi | 10.1109/IROS.2012.6386142 | |
| dc.identifier.isbn | 978-1-4673-1737-5 | |
| dc.identifier.issn | 2153-0858 | |
| dc.identifier.uri | http://hdl.handle.net/1853/49853 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.publisher.original | Institute of Electrical and Electronics Engineers | en_US |
| dc.subject | Tactile sensor array | en_US |
| dc.subject | Robotic forearms | en_US |
| dc.subject | Manipulators | en_US |
| dc.subject | Mechanical properties | en_US |
| dc.subject | Mobile robots | en_US |
| dc.subject | Object recognition | en_US |
| dc.title | Haptic Classification and Recognition of Objects Using a Tactile Sensing Forearm | en_US |
| dc.type | Text | |
| dc.type.genre | Proceedings | |
| dc.type.genre | Post-print | |
| dspace.entity.type | Publication | |
| local.contributor.author | Rehg, James M. | |
| local.contributor.author | Kemp, Charles C. | |
| local.contributor.corporatename | Healthcare Robotics Lab | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
| relation.isAuthorOfPublication | af5b46ec-ffe2-4ce4-8722-1373c9b74a37 | |
| relation.isAuthorOfPublication | e4f743b9-0557-4889-a16e-00afe0715f4c | |
| relation.isOrgUnitOfPublication | c6394b0e-6e8b-42dc-aeed-0e22560bd6f1 | |
| relation.isOrgUnitOfPublication | 66259949-abfd-45c2-9dcc-5a6f2c013bcf |
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