PROVIDENCE, R.I. [Brown University] — Researchers from Brown University and MIT have developed a method for helping robots plan for multi-step tasks by constructing abstract representations of the world around them. Their study, published in the Journal of Artificial Intelligence Research, is a step toward building robots that can think and act more like people.
Planning is a monumentally difficult thing for robots, largely because of how they perceive and interact with the world. A robot’s perception of the world consists of nothing more than the vast array of pixels collected by its cameras, and its ability to act is limited to setting the positions of the individual motors that control its joints and grippers. It lacks an innate understanding of how those pixels relate to what we might consider meaningful concepts in the world.
“That low-level interface with the world makes it really hard to do decide what to do,” said George Konidaris, an assistant professor of computer science at Brown and the lead author of the new study. “Imagine how hard it would be to plan something as simple as a trip to the grocery store if you had to think about each and every muscle you’d flex to get there, and imagine in advance and in detail the terabytes of visual data that would pass through your retinas along the way. You’d immediately get bogged down in the detail. People, of course, don’t plan that way. We’re able to introduce abstract concepts that throw away that huge mass of irrelevant detail and focus only on what is important.”
Even state-of-the-art robots aren’t capable of that kind of abstraction. When we see demonstrations of robots planning for and performing multistep tasks, “it’s almost always the case that a programmer has explicitly told the robot how to think about the world in order for it to make a plan,” Konidaris said. “But if we want robots that can act more autonomously, they’re going to need the ability to learn abstractions on their own.”
In computer science terms, these kinds of abstractions fall into two categories: “procedural abstractions” and “perceptual abstractions.” Procedural abstractions are programs made out of low-level movements composed into higher-level skills. An example would be bundling all the little movements needed to open a door — all the motor movements involved in reaching for the knob, turning it and pulling the door open — into a single “open the door” skill. Once such a skill is built, you don’t need to worry about how it works. All you need to know is when to run it. Roboticists — including Konidaris himself — have been studying how to make robots learn procedural abstractions for years, he says.
But according to Konidaris, there’s been less progress in perceptual abstraction, which has to do with helping a robot make sense of its pixelated surroundings. That’s the focus of this new research.
“Our work shows that once a robot has high-level motor skills, it can automatically construct a compatible high-level symbolic representation of the world — one that is provably suitable for planning using those skills,” Konidaris said.