University of Washington researchers have discovered a method to control the movement of a humanoid robot with signals from a human brain.
Rajesh Rao, associate professor of computer science and engineering, and his students have demonstrated that an individual can “order” a robot to move to specific locations and pick up specific objects by generating the proper brain waves that reflect the individual’s instructions. The results were presented last week at the Current Trends in Brain-Computer Interfacing meeting in Whistler, British Columbia.
“This is really a proof-of-concept demonstration,” Rao says. “It suggests that one day we might be able to use semi-autonomous robots for such jobs as helping disabled people or performing routine tasks in a person’s home.”
The controlling individual — in this case a graduate student in Rao’s lab — wears a cap dotted with 32 electrodes. The electrodes pick up brain signals from the scalp based on a technique called electroencephalography. The person watches the robot’s movements on a computer screen via two cameras, one mounted on the robot and another above it.
Currently, the “thought commands” are limited to a few basic instructions. A person can instruct the robot to move forward, choose one of two available objects, pick it up, and bring it to one of two locations. Preliminary results show 94 percent accuracy in choosing the correct object.
Objects available to be picked up are seen by the robot’s camera and conveyed to the user’s computer screen. Each object lights up randomly. When the person looks at the object that he or she wants to pick up and sees it suddenly brighten, the brain registers surprise. The computer detects this characteristic surprised pattern of brain activity and conveys the choice back to the robot, which then proceeds to pick up the selected object. A similar procedure is used to determine the user’s choice of a destination once the object has been picked up.
“One of the important things about this demonstration is that we’re using a ‘noisy’ brain signal to control the robot,” Rao says. “The technique for picking up brain signals is non-invasive, but that means we can only obtain brain signals indirectly from sensors on the surface of the head, and not where they are generated deep in the brain. As a result, the user can only generate high-level commands such as indicating which object to pick up or which location to go to, and the robot needs to be autonomous enough to be able to execute such commands.”
Rao’s team has plans to extend the research to use more complex objects and equip the robot with skills such as avoiding obstacles in a room. This will require more complicated commands from the “master’s” brain and more autonomy on the part of the robot.
“We want to get to the point of using actual objects that people might want the robot to gather, as well as having the robot move through multiple rooms,” Rao says.