And the ultimate physical test takes robots outside the sterile environments constructed for lab tests and into the messy, disorderly lives of humans. “We need to take our robots into homes,” says Abhinav Gupta, a roboticist at CMU who helped develop the new system. “We need to collect lots of data from manipulation in a real setting where the floors can be different—sometimes it can be carpet, sometimes it can be a tiled floor, sometimes it can be a wooden floor.”

When these researchers were training the robot in their homes, it came loaded with some prior knowledge. For instance, that a grasp entails seeing an object with machine vision, reaching down, and grabbing it. The question was where to grab an object. “It will choose a random location and try to close its fingers and see if it's able to successfully grasp it or not,” says Gupta. “Basically, pick it up from the floor or not.” The robot can tell if it's a successful grip thanks to a force sensor in its gripper and from seeing the object in its hand.

“Initially it's random, but after a few thousand iterations, it will learn where it is successful and where it is not,” Gupta adds. Thus a robot can teach itself with real-world objects, then use that data to inform how it tackles different things it comes across in the home. Unlike in the lab, it’s doing all this with different lighting and flooring, so it’s gathering richer data that more accurately represents the environments where robots will one day work—decluttering homes for the elderly, for instance. So once it lands in an Airbnb—an unfamiliar environment—it can adapt instead of freaking out. Here it was able to successfully grasp novel objects 62 percent of the time, while a model trained in the lab could only manage 18.5 percent.

That doesn’t mean lab testing is passé by any means; sophisticated robots that can execute tasks with accuracy down to a few millimeters are critical for research into grasping in general, which remains a big problem for robots. But those kinds of bots are both oversized and overpriced—running into the tens of thousands of dollars—for experimenting in the home. The CMU researchers pieced together this more mobile home robot for the low, low price of $3,000.

That came with compromises, like less accurate motors with centimeter, not millimeter, accuracy. That’s not great—imagine being a centimeter off-target as you go in to grab a can of soda. But “what we tried to do is model the noise,” Gupta says. “We're not only trying to learn how to grasp, but we're also trying to learn what are the errors in the controllers.” When they could model this, they could correct the robot’s slightly wayward movements appropriately.

“By factoring the noise in such uncontrolled environments and low-cost hardware, the paper shows how data collection for robotics can be taken out of the lab, which can allow for more highly scalable, diverse, and generalizable data,” says Xavier Puig, who’s working on robot learning in simulation at MIT CSAIL.

Great for robots, and great for the owners of those Airbnbs. Robots, after all, would never dare leave the toilet seat up.

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