2026-09-22T17:49:09Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/34413 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Levinson, Stephen E. Silver, Aaron 2012-09-18T21:15:43Z 2012-09-18T21:15:43Z 2012-08 2012-09-18T21:15:43Z 2012-08 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. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2012-06-11T15:28:33Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 9 thesisrefs.bib: 48375 bytes, checksum: 0b616968c04ba434e66c234f0ea005ca (MD5) results.tex: 12528 bytes, checksum: b7ab7c6f283b01a860d74f87ba998c0d (MD5) Qlearn.tex: 13774 bytes, checksum: d53b34b368541920411ae5cae0e3fc80 (MD5) platform.tex: 22309 bytes, checksum: a30eec4cb061ea75814d28a8b1318192 (MD5) bground.tex: 4924 bytes, checksum: ecdc3b40074ba2001471e637fc975c62 (MD5) pid.tex: 10690 bytes, checksum: 0758bb974427287d021bead650982f5b (MD5) intro.tex: 4437 bytes, checksum: 8d84aab5fe35df9db3aa2abeb1d8086a (MD5) ecethesis.tex: 5069 bytes, checksum: 57902e41ffc0ea9c493b88a9de5d2ce6 (MD5) Silver_Aaron.pdf: 2353699 bytes, checksum: 3e8aa289cdbb0f394110985ba0cb1390 (MD5) Made available in DSpace on 2012-09-18T21:15:43Z (GMT). No. of bitstreams: 10 Silver_Aaron.pdf: 2353699 bytes, checksum: 3e8aa289cdbb0f394110985ba0cb1390 (MD5) license.txt: 4061 bytes, checksum: 4277c842f142fef8ca901f56608b1308 (MD5) ecethesis.tex: 5069 bytes, checksum: 57902e41ffc0ea9c493b88a9de5d2ce6 (MD5) intro.tex: 4437 bytes, checksum: 8d84aab5fe35df9db3aa2abeb1d8086a (MD5) pid.tex: 10690 bytes, checksum: 0758bb974427287d021bead650982f5b (MD5) bground.tex: 4924 bytes, checksum: ecdc3b40074ba2001471e637fc975c62 (MD5) platform.tex: 22309 bytes, checksum: a30eec4cb061ea75814d28a8b1318192 (MD5) Qlearn.tex: 13774 bytes, checksum: d53b34b368541920411ae5cae0e3fc80 (MD5) results.tex: 12528 bytes, checksum: b7ab7c6f283b01a860d74f87ba998c0d (MD5) thesisrefs.bib: 48375 bytes, checksum: 0b616968c04ba434e66c234f0ea005ca (MD5) http://hdl.handle.net/2142/34413 en Copyright 2012 Aaron Silver reinforcement learning fine motor control humanoid robot iCub proportional–integral–derivative (PID) control balancing Multi-dimensional pre-programmed and learned fine motor control for a humanoid robot Electrical & Computer Eng 1933 Electrical & Computer Engr 1200 University of Illinois at Urbana-Champaign Thesis M.S. MS:Electr & Computer Eng-UIUC 10KS1200MS