July 14, 2021
A team at the Massachusetts Institute of Technology (MIT) has developed a robotic arm that can slide one arm of a vest onto a person. And that’s more impressive than it may initially seem, Fast Company reports.
It represents an early, but important, step in creating a robot that is completely dress an aging or disabled person—safely and gently handling painful, stiff, and bent joints, and other problems that affect the elderly .
Robots have actually been able to dress themselves for a decade now. Such an achievement is possible only because a robot knows the dimensions of its own body and exactly what it intends to do next. For a robot to dress someone else is an entirely different challenge—because the task requires it to intuit someone else’s next move, lest the robot make an error that might twist a wrist or dislocate an elbow.
“In this work, we focus on a planning technique,” explains Shen Li, a PhD candidate in the Interactive Robotics Group at MIT and the author of the new paper published by Science and Systems. “Robots predict human motion; then, design a plan that’s safe based upon the prediction. If I dress a kid or adult, they might have different reactions. So you have to predict what they’ll do.”
This prediction, in the human brain, is an invisible process. We don’t fully understand how a person approaches a situation like sliding a shirtsleeve onto another human.
Li and his collaborators took a stock robot arm and fit it with a 3D tracker, which can see the movement of the person waiting to be dressed. Their breakthrough is in the software, which not only recognizes someone’s position in the moment, but considers how they might move next—in order to successfully get them dressed, and not injure them in the process.
To anticipate one of, say, 100 different possible movements, the system “learns to” predict the 100 possible movements first; and create a path that ensures a person’s safety, no matter how they actually move.
“We’re not only predicting the most likely human movement, but the entire uncertain human set of the future,” Li told Fast Company—noting that this is an especially conservative approach that can mean you are getting dressed at a snail’s pace.
However, over time, the software learns from the person getting dressed. It can slowly disregard movements a person never makes—editing down the possible list to something more probable and practical.
For the next steps of research, Li would like to add a full sleeve to the vest, and develop the software to accommodate for the extra friction of pulling a garment onto an appendage. After that step is figured out, pulling on a second sleeve, or a pair of pants, will be easier.
The other big shortcoming in this research is that the current robot starts with a human fist already pulled through a sleeve hole, so the team would like to solve that issue, too—dressing a human from the earliest steps in the process. Li notes that nurses will often take a person’s hand and stick it through a sleeve, hinting that ultimately, a second robot arm could make this task a lot easier.
These may sound like baby steps of development, in a world where machine learning models seem to solve massive problems like computer vision and object recognition overnight. “How do you develop an algorithm to learn [human behavior] efficiently?” Li asks. “You can’t just have a human there doing the task [a million times].’”
Research contact: @FastCompany