How to Train a Robot (Using Artificial Intelligence and Supercomputers)

Examples of 3D point clouds synthesized by the progressive conditional generative adversarial network (PCGAN) for an assortment of object classes. PCGAN generates both geometry and color for point clouds, without supervision, through a coarse to fine training process. UT Arlington computer scientists use TACC systems to generate synthetic objects for robot training. 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.