Once the robot had been given the software, the researchers found that it did indeed move like a human. When moving slowly, it passed close to an obstacle, because it knew that it could recalculate its path without changing course too much. When moving more quickly toward the target, the robot gave obstacles a wider berth since it had less time to calculate a new trajectory.
Robots that navigate using more conventional methods may be more efficient and reliable, says Antonio Frisoli of the PERCRO Laboratory at Scuola Superiore Sant’Anna, in Pisa, Italy, who led the team that built the robot’s head. For example, a robot guided by laser range-finding and conventional route-planning algorithms would take the most direct path from point A to point B. But the team’s goal was not to compete with the fastest, most efficient robot. Rather, the researchers wanted to understand how humans navigate. We, too, says Frisoli, “adapt our trajectory according to our speed of walking.”
Applications of the technology could include “smart” wheelchairs that can navigate easily indoors, says Greenlee. A few members of the consortium have applied for a grant to follow up on this application, while one of the original partners, Cambridge Research Systems, in the U.K., is developing a head-mounted device based on the technology that could aid the visually impaired by detecting obstacles and dangers and communicating them to the wearer.
Tomaso Poggio, who heads MIT’s Center for Biological and Computational Learning and studies visual learning and scene recognition, says of the EU project, “It seems to be a trend, from neuroscience to computer science, to look at the brain for designing new systems.” He adds that we are “on the cusp of a new stage where artificial intelligence is getting information from neuroscience,” and says that there are “definitely areas of intelligence like vision, or speech understanding, or sensory-motor control, where our algorithms are vastly inferior to what the brain can do.”