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dc.contributor.authorLenz, Ian
dc.date.accessioned2016-07-05T15:30:05Z
dc.date.available2016-07-05T15:30:05Z
dc.date.issued2016-02-01
dc.identifier.otherbibid: 9597107
dc.identifier.urihttps://hdl.handle.net/1813/44317
dc.description.abstractRobotics faces many unique challenges as robotic platforms move out of the lab and into the real world. In particular, the huge amount of variety encountered in real-world environments is extremely challenging for existing robotic control algorithms to handle. This necessistates the use of machine learning algorithms, which are able to learn controls given data. However, most conventional learning algorithms require hand-designed parameterized models and features, which are infeasible to design for many robotic tasks. Deep learning algorithms are general non-linear models which are able to learn features directly from data, making them an excellent choice for such robotics applications. However, care must be taken to design deep learning algorithms and supporting systems appropriate for the task at hand. In this work, I describe two applications of deep learning algorithms and one application of hardware neural networks to difficult robotics problems. The problems addressed are robotic grasping, food cutting, and aerial robot obstacle avoidance, but the algorithms presented are designed to be generalizable to related tasks.
dc.language.isoen_US
dc.subjectRobotics
dc.subjectMachine learning
dc.subjectDeep learning
dc.titleDeep Learning For Robotics
dc.typedissertation or thesis
thesis.degree.disciplineComputer Science
thesis.degree.grantorCornell University
thesis.degree.levelDoctor of Philosophy
thesis.degree.namePh. D., Computer Science
dc.contributor.chairSaxena,Ashutosh
dc.contributor.committeeMemberSnavely,Keith Noah
dc.contributor.committeeMemberManohar,Rajit
dc.contributor.committeeMemberKnepper,Ross A


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