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Generating Synthetic Objects to Train Robots Written by AZoRoboticsJan 20 2021 Before he joined the University of Texas at Arlington as an Assistant Professor in the Department of Computer Science and Engineering and founded the Robotic Vision Laboratory there, William Beksi interned at iRobot, the world's largest producer of consumer robots (mainly through its Roomba robotic vacuum). To navigate built environments, robots must be able to sense and make decisions about how to interact with their locale. Researchers at the company were interested in using machine and deep learning to train the...

William Beksi, UT Arlington Before he joined the University of Texas at Arlington as an Assistant Professor in the Department of Computer Science and Engineering and founded the Robotic Vision Laboratory there, William Beksi interned at iRobot, the world's largest producer of consumer robots (mainly through its Roomba robotic vacuum). To navigate built environments, robots must be able to sense and make decisions about how to interact with their locale. Researchers at the company were interested in using machine and deep learning to train their robots to learn about objects, but doing so requ...

Computer scientists from UT Arlington developed a deep learning method to create realistic objects for virtual environments that can be used to train robots. The researchers used TACC's Maverick2 supercomputer to train the generative adversarial network. The network is the first that can produce colored point clouds with fine details at multiple resolutions. The team presented their results at the International Conference on 3D Vision (3DV) in Nov. 2020.