A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Boston University is working on a feedback system that lets people correct robot mistakes instantly with brain signals.
Using data from an electroencephalography (EEG) monitor that records brain activity, the system can detect if a person notices an error as a robot performs an object-sorting task.
The team’s machine-learning algorithms enable the system to classify brain waves in the space of 10 to 30 milliseconds.
While the system currently handles simple binary-choice activities, the work suggests that we could one day control robots in much more intuitive ways.
“Imagine being able to instantaneously tell a robot to do a certain action, without needing to type a command, push a button, or even say a word,” said CSAIL Director Daniela Rus.
“A streamlined approach like that would improve our abilities to supervise factory robots, driverless cars, and other technologies.”
The team currently uses a humanoid robot named Baxter from Rethink Robotics.