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Haptic Perception, Decision-making, and Learning for Manipulation with Artificial Hands

Abstract Robotic systems are outmatched by the abilities of the human hand to perceive and manipulate the world. Human hands are able to physically interact with the world to perceive, learn, and act to accomplish tasks. Limitations of robotic systems to interact with and manipulate the world diminish their usefulness. In order to advance robot end effectors, specifically artificial hands, rich multimodal tactile sensing is needed. In this work, a multi-articulating, anthropomorphic robot testbed was developed for investigating tactile sensory stimuli during finger-object interactions. The artificial finger is controlled by a tendon-driven remote actuation system that allows for modular control of any tendon-driven end effector and capabilities for ... (more)
Created Date 2016
Contributor Hellman, Randall Blake (Author) / Santos, Veronica J (Advisor) / Artemiadis, Panagiotis K (Committee member) / Berman, Spring (Committee member) / Helms Tillery, Stephen I (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Subject Engineering / Robotics / Decision Making / Functional contour following / Haptic perception / Reinforcement Learning / Robot hands / Robot touch perception
Type Doctoral Dissertation
Extent 181 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Mechanical Engineering 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis