Amazon Robot Contest May Accelerate Warehouse Automation
Amazon Robot Contest May Accelerate Warehouse Automation
Robots will use the latest computer-vision and
machine-learning algorithms to try to perform the work done by humans in vast
fulfillment centers.
By Will Knight on March 25, 2015
Packets of Oreos, boxes of crayons, and squeaky dog toys
will test the limits of robot vision and manipulation in a competition this
May. Amazon is organizing the event to spur the development of more
nimble-fingered product-packing machines.
Participating robots will earn points by locating
products sitting somewhere on a stack of shelves, retrieving them safely, and
then packing them into cardboard shipping boxes. Robots that accidentally crush
a cookie or drop a toy will have points deducted. The people whose robots earn
the most points will win $25,000.
Amazon has already automated some of the work done in its
vast fulfillment centers. Robots in a few locations send shelves laden with
products over to human workers who then grab and package them. These mobile
robots, made by Kiva Systems, a company that Amazon bought in 2012 for $678
million, reduce the distance human workers have to walk in order to find
products. However, no robot can yet pick and pack products with the speed and
reliability of a human. Industrial robots that are already widespread in
several industries are limited to extremely precise, repetitive work in highly
controlled environments.
Pete Wurman, chief technology officer of Kiva Systems,
says that about 30 teams from academic departments around the world will take
part in the challenge, which will be held at the International Conference on
Robotics and Automation in Seattle (ICRA 2015). In each round, robots will be
told to pick and pack one of 25 different items from a stack of shelves
resembling those found in Amazon’s warehouses. Some teams are developing their
own robots, while others are adapting commercially available systems with their
own grippers and software.
The 25 items that participating robots will need to
retrieve from shelves.
The challenge facing the robots in Amazon’s contest will
be considerable. Humans have a remarkable ability to identify objects, figure
out how to manipulate them, and then grasp them with just the right amount of
force. This is especially hard for machines to do if an object is unfamiliar,
awkwardly shaped, or sitting on a dark shelf with a bunch of other items. In
the Amazon contest, the robots will have to work without any remote guidance
from their creators.
“We tried to pick out a variety of different products
that were representative of our catalogue and that pose different kinds of
grasping challenges,” Wurman said. “Like plastic wrap; difficult-to-grab little
dog toys; things you don’t want to crush, like the Oreos.”
The video below shows the approach taken by a team at the
University of Colorado. The team is using off-the-shelf software and building a
robot arm specialized for the task, says Dave Coleman, a PhD student involved.
The contest could offer a way to judge the progress that
has been made in the past few years, when some cheaper, safer, and more
adaptable robots have emerged (see “How Technology Is Destroying Jobs”) thanks
to advances in the technologies underlying machine dexterity. New types of
robot manipulators are making machines less ham-handed at picking up fiddly or
awkward objects, for example. Several startups are developing robot hands that
seek to copy the flexibility and sense of touch found in human digits. Progress
in machine learning could help robots perform far more sophisticated object
manipulation in coming years.
A key breakthrough in this area came in 2006, when a
group of researchers led by Andrew Ng, then at Stanford and now at Baidu,
devised a way for robots to work out how to manipulate unfamiliar objects.
Instead of writing rules for how to grasp a specific object or shape, the
researchers enabled their robot to study thousands of 3-D images and learn to
recognize which types of grip would work for different shapes. This allowed it
to figure out suitable grips for new objects.
In recent years, robotics researchers have increasingly
used a powerful machine-learning approach known as deep learning to improve
these capabilities (see “10 Breakthrough Technologies 2013: Deep Learning”).
Ashutosh Saxena, a member of Ng’s team at Stanford and now an assistant
professor at Cornell University, is using deep learning to train a robot that
will take part in the Amazon challenge. He is working with one of his students,
Ian Lenz.
While the Amazon challenge might seem simple, Saxena
believes it could quickly make an impact in the real world. “If robots are able
to handle even the light types of grasping tasks the contest proposes,” he
says, “we could actually start to see a lot of robots helping people with
different tasks.”
Why It Matters
More dexterous robots could take on many routine jobs,
from stacking shelves to serving food.
Comments
Post a Comment