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Grasping in 6DoF: An Orthographic Approach to Generalized Grasp Affordance Predictions

Rios Munoz, Mario (2021) Grasping in 6DoF: An Orthographic Approach to Generalized Grasp Affordance Predictions. Master's Thesis / Essay, Artificial Intelligence.

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Abstract

Grasp detection research focuses at the moment on finding neural networks that given a RGB-D image or point cloud, yield a parametric grasp description that can be used to firmly grip target objects. There is a need for these models to be small and ecient, such that they can be used in embedded hardware. Furthermore these models tend to only work for top-down views, which highly restrict the ways objects can be grasped. In this work, we focus on improving an existing shallow network, GG-CNN, and propose a new orthographic pipeline to enable the use of these models independently of the orientation of the camera. We make our implementation available on GitHub.

Item Type: Thesis (Master's Thesis / Essay)
Supervisor name: Mohades Kasaei, S.H. and Schomaker, L.R.B.
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 08 Apr 2021 08:30
Last Modified: 08 Apr 2021 08:30
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/24221

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