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Title:Multi-dimensional pre-programmed and learned fine motor control for a humanoid robot
Author(s):Silver, Aaron
Advisor(s):Levinson, Stephen E.
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):reinforcement learning
fine motor control
humanoid robot
proportional–integral–derivative (PID) control
Abstract:One of the biggest questions in modern electrical and computer engineering is that of computational intelligence. Can an artificially created mechanism, such as a humanoid robot, become intelligent, adaptable, and possess the ability to learn to classify the world in terms of language as humans do? This thesis discusses an effort to explore one small piece of this puzzle, the use of sensory-motor interaction with the physical world. We postulate that the ability to manipulate and interact with the environment around oneself is integral to achieving intelligence and language. The first part of this study employs a sophisticated iCub humanoid robot and classical control techniques to determine if the robot is capable of performing the necessary fine-motor controlled tasks. The second part of this study goes on to determine if machine reinforcement learning techniques are able to give this robot the same capability.
Issue Date:2012-09-18
Rights Information:Copyright 2012 Aaron Silver
Date Available in IDEALS:2012-09-18
Date Deposited:2012-08

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