US researchers exploring AI to train robots

William Beksi, assistant professor in UT Arlington’s Department of Computer Science and Engineering and founder of the university’s Robotic Vision Laboratory, is leading the research with a group including six PhD Computer Science students. 

Having previously interned at consumer robot producer iRobot, where researchers were interested in using machine and deep learning to train robots, Beksi said he was particularly interested in developing algorithms that enable machines to learn from their interactions with the physical world and autonomously acquire skills necessary to execute high-level tasks.

Where efforts to train robots using images with human-centric perspectives had previously failed, Beksi looked to generative adversial networks (GANs). This involves two neural networks contesting with each other in a game until the ‘generator’ of new data can fool a ‘discriminator’. 

Once trained, such a network could enable the creation of an infinite number of possible rooms or outdoor environments, researchers explained, with different kinds of objects identifiable to a person and a robot with recognisable dimensions and characteristics.