Improving robotic grasping performance using machine learning techniques Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/xd07gw31h

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  • Robots are being utilized in ever more complex tasks and environments to help humans with difficult or dangerous tasks. However, robotic grasping is still in its infancy and is one of the limiting factors which prevent the deployment of robots in the home and other assisted living scenarios. Traditional methods for grasp planning use grasp metrics, which are numerical computations of the kinematic arrangement of the hand and object. However, they are insufficient alone for accounting for all of the variables involved in the grasping process shown by their poor performance when implemented on a robotic platform. We use grasp testing data, along with a machine learning algorithm, in order to learn the complex relationship among all of the grasp metrics so as to improve grasp prediction performance. We then evaluate the resulting machine algorithm to validate the results and compare them to the individual metrics and state of the art grasp planners.
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