In the Future, Warehouse Robots Will Learn on Their Own
In the Future, Warehouse Robots Will Learn on Their Own
By CADE METZ SEPT. 10, 2017
BERKELEY, Calif. — The robot was perched over a bin
filled with random objects, from a box of instant oatmeal to a small toy shark.
This two-armed automaton did not recognize any of this stuff, but that did not
matter. It reached into the pile and started picking things up, one after
another after another.
“It figures out the best way to grab each object, right
from the middle of the clutter,” said Jeff Mahler, one of the researchers
developing the robot inside a lab at the University of California, Berkeley.
For the typical human, that is an easy task. For a robot,
it is a remarkable talent — something that could drive significant changes
inside some of the world’s biggest businesses and further shift the market for
human labor.
Today, robots play important roles inside retail giants
like Amazon and manufacturing companies like Foxconn. But these machines are
programmed for very specific tasks, like moving a particular type of container
across a warehouse or placing a particular chip on a circuit board. They can’t
sort through a big pile of stuff, or accomplish more complex tasks. Inside
Amazon’s massive distribution centers — where sorting through stuff is the
primary task — armies of humans still do most of the work.
The Berkeley robot was all the more remarkable because it
could grab stuff it had never seen before. Mr. Mahler and the rest of the
Berkeley team trained the machine by showing it hundreds of purely digital
objects, and after that training, it could pick up items that weren’t
represented in its digital data set.
“We’re learning from simulated models and then applying
that to real work,” said Ken Goldberg, the Berkeley professor who oversees the
university’s automation lab.
The robot was far from perfect, and it could be several
years before it is seen outside research labs. Though it was equipped with a
suction cup or a parallel gripper — a kind of two-fingered hand — it could
reliably handle only so many items. And it could not switch between the cup and
the gripper on the fly. But the techniques used to train it represented a
fundamental shift in robotics research, a shift that could overhaul not just
Amazon’s warehouses but entire industries.
Rather than trying to program behavior into their robot —
a painstaking task — Mr. Mahler and his team gave it a way of learning tasks on
its own. Researchers at places like Northeastern University, Carnegie Mellon
University, Google and OpenAI — the artificial intelligence lab founded by
Tesla’s chief executive, Elon Musk — are developing similar techniques, and
many believe that such machine learning will ultimately allow robots to master
a much wider array of tasks, including manufacturing.
“This can extend to tasks of assembly and more complex
operations,” said Juan Aparicio, head of advanced manufacturing automation at
the German industrial giant Siemens, which is helping to fund the research at
Berkeley. “That is the road map.”
Physically, the Berkeley robot was nothing new. Mr.
Mahler and his team were using existing hardware, including two robotic arms
from the Swiss multinational ABB and a camera that captured depth.
What was different was the software. It demonstrated a
new use for what are called neural networks. Loosely based on the network of
neurons in the human brain, a neural network is a complex algorithm that can
learn tasks by analyzing vast amounts of data. By looking for patterns in
thousands of dog photos, for instance, a neural network can learn to recognize
a dog.
Over the past five years, these algorithms have radically
changed the way the internet’s largest companies build their online services,
accelerating the development of everything from image and speech recognition to
internet search. But they can also accelerate the development of robotics.
The Berkeley team began by scouring the internet for CAD
models, short for computer-aided design. These are digital representations of
physical objects. Engineers, physicists and designers build them when running
experiments or creating new products. Using these models, Mr. Mahler and his
team generated many more digital objects, eventually building a database of
more than seven million items. Then they simulated the physics of each item,
showing the precise point where a robotic arm should pick it up.
That was a large task, but the process was mostly
automated. When the team fed these models into a neural network, it learned to
identify a similar point on potentially any digital object with any shape. And
when the team plugged this neural network into the two-armed robot, it could do
the same with physical objects.
When facing a single everyday object with cylindrical or
at least partly planar surfaces — like a spatula, a stapler, a cylindrical
container of Froot Loops or even a tube of toothpaste — it could typically pick
it up, with success rates often above 90 percent. But percentages dropped with more
complex shapes, like the toy shark.
What’s more, when the team built simulated piles of
random objects and fed those into the neural network, it could learn to lift
items from physical piles, too. Researchers at Brown University and
Northeastern are exploring similar research, and the hope is that this kind of
work can be combined with other methods.
Like Siemens and the Toyota Research Institute, Amazon is
helping to fund the work at Berkeley, and it has an acute need for this kind of
robot. For the past three years, the company has run a contest in which
researchers seek to solve the “pick and place” problem. But the promise of
machine-learning methods like the one used at Berkeley is that they can
eventually extend to so many other areas, including manufacturing and home
robotics.
“Picking an object up is the first thing you want a
manipulator robot to do,” said Stefanie Tellex, a professor at Brown. “A lot of
more sophisticated behavior begins with that. If you can’t pick it up, game
over.”
The research demonstrated how a task learned in the
digital world can be transferred to the physical. Since the camera on
Berkeley’s robot could see depth, it captured three-dimensional images that
were not unlike the CAD models the team uses to train its neural network.
Other researchers are developing ways for robots to learn
directly from physical experience. For example, at Google, using an algorithmic
technique called reinforcement learning, robots are training themselves to open
doors through extreme trial and error. But this kind of physical training is
both time consuming and expensive. Digital training is more efficient.
For this reason, some organizations are hoping to train
robots using complex virtual worlds — digital recreations of our physical
environment. If a system can train itself to navigate a car racing game like
Grand Theft Auto, the thinking goes, it can navigate real roads.
This is still largely theory. But at places like Berkeley
and Northeastern, researchers are showing that digital learning can indeed make
the leap into the real world.
“This is a challenge,” said Rob Platt, a professor at
Northeastern. “But it’s a challenge we’re dealing with.”
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