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dc.contributor.authorRenner, Michael Roberten_US
dc.date.accessioned2014-03-14T20:43:40Z
dc.date.available2014-03-14T20:43:40Z
dc.date.issued2007-08-10en_US
dc.identifier.otheretd-08172007-151826en_US
dc.identifier.urihttp://hdl.handle.net/10919/34602
dc.description.abstractMilitary, Medical, Exploratory, and Commercial robots have much to gain from exchanging wheels for legs. However, the equations of motion of dynamic bipedal walker models are highly coupled and non-linear, making the selection of an appropriate control scheme difficult. A temporal difference reinforcement learning method known as Q-learning develops complex control policies through environmental exploration and exploitation. As a proof of concept, Q-learning was applied through simulation to a benchmark single pendulum swing-up/balance task; the value function was first approximated with a look-up table, and then an artificial neural network. We then applied Evolutionary Function Approximation for Reinforcement Learning to effectively control the swing-leg and torso of a 3 degree of freedom active dynamic bipedal walker in simulation. The model began each episode in a stationary vertical configuration. At each time-step the learning agent was rewarded for horizontal hip displacement scaled by torso altitude--which promoted faster walking while maintaining an upright posture--and one of six coupled torque activations were applied through two first-order filters. Over the course of 23 generations, an approximation of the value function was evolved which enabled walking at an average speed of 0.36 m/s. The agent oscillated the torso forward then backward at each step, driving the walker forward for forty-two steps in thirty seconds without falling over. This work represents the foundation for improvements in anthropomorphic bipedal robots, exoskeleton mechanisms to assist in walking, and smart prosthetics.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartetd_part3.pdfen_US
dc.relation.haspartetd_part2.pdfen_US
dc.relation.haspartetd_part1.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectDynamic Bipedal Walkingen_US
dc.subjectReinforcement Learningen_US
dc.subjectQ-Learningen_US
dc.subjectTorsoen_US
dc.subjectNEAT+Qen_US
dc.titleMachine Learning Simulation: Torso Dynamics of Robotic Bipeden_US
dc.typeThesisen_US
dc.contributor.departmentMechanical Engineeringen_US
thesis.degree.nameMaster of Engineeringen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
dc.contributor.committeechairGranata, Kevin P.en_US
dc.contributor.committeememberHong, Dennis W.en_US
dc.contributor.committeememberKasarda, Mary E. F.en_US
dc.contributor.committeememberReinholtz, Charles F.en_US
dc.contributor.committeememberSandu, Corinaen_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-08172007-151826/en_US
dc.date.sdate2007-08-17en_US
dc.date.rdate2007-08-22
dc.date.adate2007-08-22en_US


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