Algorithm Tells Robots Where Nearby Humans Are Headed

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As a solution, Lasota and Shah devised a “partial trajectory” algorithm that aligns segments of a person’s trajectory in real-time with a library of previously collected reference trajectories. Importantly, the new algorithm aligns trajectories in both distance and timing, and in so doing, is able to accurately anticipate stops and overlaps in a person’s path.

“Say you’ve executed this much of a motion,” Lasota explains. “Old techniques will say, ‘this is the closest point on this representative trajectory for that motion.’ But since you only completed this much of it in a short amount of time, the timing part of the algorithm will say, ‘based on the timing, it’s unlikely that you’re already on your way back, because you just started your motion."

The team tested the algorithm on two human motion datasets: one in which a person intermittently crossed a robot’s path in a factory setting (these data were obtained from the team’s experiments with BMW), and another in which the group previously recorded hand movements of participants reaching across a table to install a bolt that a robot would then secure by brushing sealant on the bolt.

For both datasets, the team’s algorithm was able to make better estimates of a person’s progress through a trajectory, compared with two commonly used partial trajectory alignment algorithms. Furthermore, the team found that when they integrated the alignment algorithm with their motion predictors, the robot could more accurately anticipate the timing of a person’s motion. In the factory floor scenario, for example, they found the robot was less prone to freezing in place, and instead smoothly resumed its task shortly after a person crossed its path.

While the algorithm was evaluated in the context of motion prediction, it can also be used as a preprocessing step for other techniques in the field of human-robot interaction, such as action recognition and gesture detection. Shah says the algorithm will be a key tool in enabling robots to recognize and respond to patterns of human movements and behaviors. Ultimately, this can help humans and robots work together in structured environments, such as factory settings and even, in some cases, the home.

“This technique could apply to any environment where humans exhibit typical patterns of behavior,” Shah says. “The key is that the [robotic] system can observe patterns that occur over and over, so that it can learn something about human behavior. This is all in the vein of work of the robot better understand aspects of human motion, to be able to collaborate with us better.”

This research was funded, in part, by a NASA Space Technology Research Fellowship and the National Science Foundation.

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