Title:
Rapid Categorization of Object Properties from Incidental Contact with a Tactile Sensing Robot Arm,
Rapid Categorization of Object Properties from Incidental Contact with a Tactile Sensing Robot Arm,
| dc.contributor.author | Bhattacharjee, Tapomayukh | en_US |
| dc.contributor.author | Kapusta, Ariel | 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 | 2013-12-18T21:36:28Z | |
| dc.date.available | 2013-12-18T21:36:28Z | |
| dc.date.issued | 2013-10 | |
| dc.description | ©2013 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 IEEE-RAS International Conference on Humanoid Robots, Humanoids in the Real World, October 15-17, 2013, Atlanta, Georgia, USA. | en_US |
| dc.description.abstract | We demonstrate that data-driven methods can be used to rapidly categorize objects encountered through incidental contact on a robot arm. Allowing incidental contact with surrounding objects has benefits during manipulation such as increasing the workspace during reaching tasks. The information obtained from such contact, if available online, can potentially be used to map the environment and help in manipulation tasks. In this paper, we address this problem of online categorization using incidental contact during goal oriented motion. In cluttered environments, the detailed internal structure of clutter can be difficult to infer, but the environment type is often apparent. In a randomized cluttered environment of known object types and “outliers”, our approach uses Hidden Markov Models to capture the dynamic robot-environment interactions and to categorize objects based on the interactions. We combined leaf and trunk objects to create artificial foliage as a test environment. We collected data using a skin-sensor on the robot’s forearm while it reached into clutter. Our algorithm classifies the objects rapidly with low computation time and few data-samples. Using a taxel-by-taxel classification approach, we can successfully categorize simultaneous contacts with multiple objects and can also identify outlier objects in the environment based on the prior associated with an object’s likelihood in the given environment. | en_US |
| dc.identifier.citation | Rapid Categorization of Object Properties from Incidental Contact with a Tactile Sensing Robot Arm, Tapomayukh Bhattacharjee, Ariel Kapusta, James M. Rehg, and Charles C. Kemp, IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2013. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/49847 | |
| 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 | Hidden Markov models | en_US |
| dc.subject | HMM | en_US |
| dc.subject | Tactile sensing robot arm | en_US |
| dc.subject | Rapid categorization of object properties | en_US |
| dc.title | Rapid Categorization of Object Properties from Incidental Contact with a Tactile Sensing Robot Arm, | 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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