Easy for you, tough for a robot
You’re sitting across from a robot, staring at a chess board. Finally, you see a move that looks pretty good. You reach out and push your queen forward. Now it’s the robot’s turn. Its computer brain calculates a winning move in a fraction of a second. But when it tries to grab a knight, it knocks down a row of pawns. Game over.
“Robots are klutzes,” says Ken Goldberg. He’s an engineer and artificial intelligence (AI) expert at the University of California, Berkeley. A computer can easily defeat a human grandmaster at the game of chess by coming up with better moves. Yet a robot has trouble picking up an actual chess piece.
This is an example of Moravec’s paradox. Hans Moravec is a roboticist at Carnegie Mellon University in Pittsburgh, Penn., who also writes about AI and the future. Back in 1988, he wrote a book that noted how reasoning tasks that seem hard to people are fairly easy to program into computers. Meanwhile, many tasks that come easily to us — like moving around, seeing or grasping things — are quite hard to program.
It’s “difficult or impossible” for a computer to match a one-year-old baby’s skills in these areas, he wrote. Though computers have advanced by leaps and bounds since then, babies and kids still beat machines at these types of tasks.
It turns out that the tasks we find easy aren’t really “easy” at all. As you walk around your house or pick up and move a chess piece, your brain is performing incredible feats of calculation and coordination. You just don’t notice it because you do it without thinking.
Let’s take a look at several tasks that are easy for kids but not for robots. For each one, we’ll find out why the task is actually so hard. We’ll also learn about the brilliant work engineers and computer scientists are doing to design new AI that should help robots up their game.
Task 1: Pick stuff up
Goldberg has something in common with robots. He, too, is a klutz. “I was the worst kid at sports. If you threw me a ball, I was sure to drop it,” he says. Perhaps, he ponders, that’s why he wound up studying robotic grasping. Maybe he’d figure out the secret to holding onto things.
He’s discovered that robots (and clumsy humans) face three challenges in grabbing an object. Number one is perception. That’s the ability to see an object and figure out where it is in space. Cameras and sensors that measure distance have gotten much better at this in recent years. But robots still get confused by anything “shiny or transparent,” he notes.
The second challenge is control. This is your ability to move your hand accurately. People are good at this, but not perfect. To test yourself, Goldberg says, “Reach out, then touch your nose. Try to do it fast!” Then try a few more times. You likely won’t be able to touch the exact same spot on your nose every single time. Likewise, a robot’s cameras and sensors won’t always be in perfect sync with its moving “hand.” If the robot can’t tell exactly where its hand is, it could miss something or drop it.
Physics poses the final challenge. To grasp something, you must understand how that object could shift when you touch it. Physics predicts that motion. But on small scales, this can be unpredictable. To see why, put a pencil on the floor, then give it a big push. Put it back in its starting place and try again. Goldberg says, “If you push it the same way three times, the pencil usually ends up in a different place.” Very tiny bumps on the floor or the pencil may change the motion.
Despite these challenges, people and other animals grasp things all the time — with hands, tentacles, tails and mouths. “My dog Rosie is pretty good at grasping anything in our house,” says Goldberg. Millions of years of evolution provided brains and bodies with ways to adapt to all three challenges. We tend to use what Goldberg calls “robust grips.” These are secure grips that work even if we run into problems with perception, control or physics. For example, if a toddler wants to pick up a block to stack it, she won’t try to grab a corner, which might slip out of her grasp. Instead, she’s learned to put her fingers on the flat sides.
To help robots learn robust grips, Goldberg’s team set up a virtual world. Called DexNet, it’s like a training arena for a robot’s AI. The AI model can practice in the virtual world to learn what types of grasps are most robust for what types of objects. The DexNet world contains more than 1,600 different virtual 3-D objects and five million different ways to grab them. Some grasps use a claw-like gripper. Others use a suction cup. Both are common robot “hand” types.
In a virtual world, a system can have perfect perception and control, and a perfect understanding of physics. So to be more like the real world, the team threw in some randomness. For each grasp, they shifted either the object or grabber just a tad. Then they checked to see if the grasp would still hold the object. Every grasp got a score based on how well it held onto an object throughout such random changes.
After a robot has completed this training, it can figure out its own robust grasp for a real-world object it has never seen before.
Task 2: Get around the world