Bryn Nelson Columnist

Don’t look now, but that robot might be tailing you.

Following someone for any length of time has been a huge challenge for robots, notwithstanding WALL-E, R2-D2 and other clever movie versions. But by incorporating subtle behavioral cues that humans use to indicate where they’re going, a team of researchers at the University of California at Davis hopes to close the artificial intelligence gap with a system that helps robots easily respond to directional signals.

“The fundamental problem we were trying to look into is how to get a robot to follow people around, in buildings or in work environments, or possibly trying to get robots to follow other robots around,” said Sanjay Joshi, an associate professor of mechanical and aeronautical engineering at the University of California at Davis. With an accurate system in hand, robots could be programmed to trail doctors on their rounds, construction workers to a building site or senior citizens requiring special assistance.

Traditional following programs have relied on two techniques to guide the internal controllers of robots: one based on physics and another on images.

The physics model, Joshi said, uses the leader’s speed and direction to calculate where it is headed and then directs the follower to move to that spot. “You want to take the robot to where the leader will be in the future, time and time again, and that allows it to track the leader,” he said.

For the second technique, the follower periodically looks to see where the leader is and then heads to the position depicted in that image.

The physics model often cannot account for sudden blind turns, however, and the imaging model struggles once the leader leaves the field of view.

Developing a 'sixth sense'
Humans, on the other hand, have learned to pick up on telling body language, like the well-established observation that people tend to turn their heads slightly to the left or right just before heading off in that direction.

The trick for Joshi’s team has been working out a system that allows robots to identify and use as many of those conscious and unconscious behavioral cues as possible. “We really were trying to understand how much of the behavior cues add to the accuracy and reliability of the following,” he said.

Eventually, Joshi said, physiologists and psychologists could collaborate with computer scientists and engineers to teach robots how to automatically identify the facial or body movements or physiological reactions that come naturally to us.

“It’s building up a sixth sense about movement,” he said.

If someone is driving down a highway and sees a car moving aggressively behind her, for example, she can often sense from its movements whether it will attempt to pass or cut in front of her. That “sixth sense” is shaped by a wealth of experience observing how other drivers behave.

“The question is, how do we get robots to use that?” Joshi said.

Before answering that question, though, researchers have had to address the underlying problem of how to get robots to reliably identify and respond to basic cues.

“You have to make the computer identify the pattern of images to know, ‘This is a head turn,’ and then use the identification of that head turn in your controller,” Joshi said. And that problem can be further divided into the question of identifying a cue versus properly responding to it.

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