Title:
Rapid haptic perception using force and thermal sensing

dc.contributor.advisor Kemp, Charles C.
dc.contributor.author Bhattacharjee, Tapomayukh
dc.contributor.committeeMember Rehg, James M.
dc.contributor.committeeMember Ting, Lena H.
dc.contributor.committeeMember Liu, C. Karen
dc.contributor.committeeMember Christensen, Henrik I.
dc.contributor.department Biomedical Engineering (Joint GT/Emory Department)
dc.date.accessioned 2018-08-20T15:33:23Z
dc.date.available 2018-08-20T15:33:23Z
dc.date.created 2017-08
dc.date.issued 2017-08-01
dc.date.submitted August 2017
dc.date.updated 2018-08-20T15:33:23Z
dc.description.abstract Tactile sensing can enable a robot to infer properties of its surroundings. Recent research has focused on robots that haptically perceive the world through exploratory behaviors that occur over tens of seconds. During manipulation, many opportunities arise for robots to gather information about the environment from brief (<= 2 seconds) contact due to simple motions (e.g., linear). The goal of our work was to enable robots to infer haptic properties under these conditions using force and thermal sensing. We used a data-driven approach with various machine learning methods. Key challenges were obtaining adequate haptic data for training and developing methods that performed well on haptic data that differed from the training data due to common real-world phenomena. For example, haptic sensory signals vary significantly due to the robot, including its velocity, stiffness, and sensor temperature. To collect suitable data, we used a variety of platforms, including simplified robots, handheld human-operated devices, and a mobile robot. We also generated synthetic data with physics-based models. Through careful empirical evaluation, we identified machine learning methods that better handled common signal variations. We also used physics-based models to characterize common perceptual ambiguities and predict the performance of data-driven methods. Overall, our research demonstrates the feasibility of robots inferring haptic properties from brief contact with objects in human environments. By using force and thermal sensing, our methods rapidly recognized materials, detected when objects moved, detected contact with people, and inferred other properties of the robot’s surroundings.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/60181
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Haptic perception
dc.subject Tactile sensing
dc.subject Manipulation
dc.subject Robotics
dc.subject Machine learning
dc.subject Classification
dc.subject k-NN
dc.subject SVM
dc.subject HMM
dc.subject LSTM
dc.subject Deep learning
dc.subject Force sensing
dc.subject Thermal sensing
dc.subject Physics-based models
dc.title Rapid haptic perception using force and thermal sensing
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Kemp, Charles C.
local.contributor.corporatename Wallace H. Coulter Department of Biomedical Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication e4f743b9-0557-4889-a16e-00afe0715f4c
relation.isOrgUnitOfPublication da59be3c-3d0a-41da-91b9-ebe2ecc83b66
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level Doctoral
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