Processors
used in gaming and supercomputers could give robots much more human-like minds,
enabling them to navigate the skies and explore Mars autonomously
IT'S second nature for us to follow an airplane across the sky, or to
walk around a rock we see in our path. It's not so easy for robots – you just
have to watch $16,000 robots play football to realise how hard it is for them
to kick a rolling ball. In contrast, our brains handle streams of visual
information seamlessly, picking out obstacles and navigating us around them.
So how do we make robot brains more like ours? One way might be to
change the type of processor they use. Until now, robots have always been
fitted with central processing units (CPUs), just like most PCs. Such
units are very good at crunching small streams of data fast, but they can only
do one thing at a time.
In contrast, graphics processing units (GPUs), which are heavily
used in supercomputers and gaming, can handle larger data sets more
quickly, and deal with several of them at once. This is how the human brain
works, and even though we process some tasks millions of times more slowly than
does a computer, the amount of information our brains can handle is vast. But
until quite recently, GPUs have been too big and expensive to use in robots.
Now a neuroscience and robotics start-up called Neurala in Cambridge,
Massachusetts, has built robot brains using GPUs. It says they run roughly 10
times as fast as those built on CPUs.
I watched a software simulation at the Neuromorphics Lab at
Boston University. A virtual rover is given a basic route across the surface of
a digital Mars and it sets off without hesitation, spotting the rocks in its
way as it goes.
The robot's brain processes visual information in real time, enabling it
to do more than simply navigate from one spot to another. This means robots
could one day be trusted to make their own decisions when navigating changing
terrain on Mars.
Mark Motter of NASA's Langley Research Center in Hampton, Virginia, says
Neurala's approach highlights the difference between automation – in which a
detailed, prescribed plan is executed by a machine – and autonomy, where a
machine is free to make its own decisions on how to reach its goal. Neurala
aims to mimic how human brains recognise objects, accumulate experience and
make judgements, Motter says. "This is an interesting approach to
autonomy."
Neurala's robot vision system also mimics a trick the human eye uses,
called foveation. The fovea is a region of the retina that is thickly populated
with visual receptors, and gives you very clear central vision. In a similar
way, the robot's vision system focuses on specific points of the scene to build
up a picture of its environment instead of trying to process everything it sees
through the camera all at once. This still results in a large amount of data
but reduces the overall load on the robot's brain.
That ability to process visual information in real time means Neurala's
brain can do more than just guide a robot from one spot to another. On its way,
it could be carrying out basic science, classifying the rocks it sees and
flagging unusual ones for further investigation, for example, or searching for
signs of water and minerals.
There is a reason why the most advanced rover currently in existence,
Curiosity, is rigidly controlled by NASA operators: space equipment in general
is fiendishly expensive, so the agency has to be sure the chances of anything going
wrong are tiny.
"If I'm going to send a rocket that costs billions of dollars, I
want to be sure that every millimetre I travel is gonna be safe," says Max
Versace, Neurala's CEO and head of Boston's Neuromorphics Lab. But as
commercial space flight heats up and costs go down, he thinks the future lies
in swarms of low-cost robots (see "Termite robots build castles with no
human help"). "We need similar machinery to the biological brain to
do that," Versace says.
Understanding and reacting to visual information in real time like a
human is a hard computer science problem, says John Owens, who studies parallel
computing at the University of California, Davis. "It's very exciting that
they're making progress in this."
Smarter drones would be useful too. If uncrewed aerial vehicles (UAVs)
are ever to share airspace with piloted craft, they'll need to be able to work
with a high degree of autonomy.
Earlier this year, the Federal Aviation Authority (FAA) announced the
creation of six UAV testing areas around the country, where researchers will
develop the kinds of systems that will allow autonomous craft to join the
national airspace, safely sensing and avoiding other aircraft. Under FAA
regulations, civilian drones in the US are currently permitted to fly only
under 400 feet (122 metres).
"You have to be able to tell the FAA that you can put this thing up
in the air at 15,000 feet and guarantee, even if you put a million of them in
the air, that you will not hit a passenger aircraft," says Jeremy Wurbs, a
graduate student in Versace's lab, who is working on adapting Neurala's system
to this task.
Robots could benefit from advances in smartphone technology too. The
devices are currently the main battleground for GPUs, forcing manufacturers to
push for smaller, denser designs. The full version of Neurala's system
currently runs across a handful of powerful desktop computers, networked
together. Smartphone advances will provide hardware that is small enough to
power mobile, autonomous robots in just a few years, Versace says.
Neurala is already working with existing mobile GPUs. The firm has
developed a prototype app that will run its vision system on an iPad's graphics
card. It will act as an external brain to boost the navigation of any robot
whose camera can talk to the iPad. Simple robots could be augmented by the
computing power that many of us carry around, which gets ever more likely as
GPUs become smaller and more powerful.
"We are designing technology for Mars," says Versace,
"but our goal is to bring it back to Earth."
This article
appeared in print under the headline "Sharing the skies"
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