August 2, 2017 --C-3PO's fluency in more than 6 million forms of communication in "Star Wars" set a high bar for human-robot interaction, and the field has been struggling to catch up ever since. They started in the factories, taking over physically demanding and repetitive tasks. Now robots are moving into hospitals, shopping malls, even the International Space Station, and experts don't expect their expansion into human spaces to slow down anytime soon. "Even 10 years ago, the primary use of the robots was in the dangerous, dirty, and dull work," says Julie Shah, an engineering professor at the Massachusetts Institute of Technology in Cambridge, Mass. "You'd deploy them to operate remotely from people, but [now] robots are integrating into all aspects of our lives relatively quickly."

Try again robot, you're doing it wrong. A brain-computer interface lets people correct robots' mistakes using the power of their thoughts. The system uses electroencephalography (EEG) to measure a person's brain signals as they watch a robot work. When it detects a signal suggesting the person has witnessed a mistake, it alters the robot's course. The system could be used to let humans control industrial robots simply by observing them.

Most of machine learning approaches have stemmed from the application of minimizing the mean squared distance principle, based on the computationally efficient quadratic optimization methods. However, when faced with high-dimensional and noisy data, the quadratic error functionals demonstrated many weaknesses including high sensitivity to contaminating factors and dimensionality curse. Therefore, a lot of recent applications in machine learning exploited properties of non-quadratic error functionals based on $L_1$ norm or even sub-linear potentials corresponding to quasinorms $L_p$ ($0