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dc.contributor.authorBhattacharjee, Tapomayukhen_US
dc.contributor.authorKapusta, Arielen_US
dc.contributor.authorRehg, James M.en_US
dc.contributor.authorKemp, Charles C.en_US
dc.date.accessioned2013-12-18T21:36:28Z
dc.date.available2013-12-18T21:36:28Z
dc.date.issued2013-10
dc.identifier.citationRapid 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.urihttp://hdl.handle.net/1853/49847
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.descriptionPresented 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.abstractWe 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.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectHidden Markov modelsen_US
dc.subjectHMMen_US
dc.subjectTactile sensing robot armen_US
dc.subjectRapid categorization of object propertiesen_US
dc.titleRapid Categorization of Object Properties from Incidental Contact with a Tactile Sensing Robot Arm,en_US
dc.typeProceedingsen_US
dc.typePost-printen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Healthcare Robotics Laben_US
dc.contributor.corporatenameGeorgia Institute of Technology. Computational Perception Laben_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Robotics and Intelligent Machinesen_US
dc.publisher.originalInstitute of Electrical and Electronics Engineersen_US


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