TechInformed
For use across multiple domains, robots need to operate untethered and exist within an ecosystem that connects to other machines and people – and simulations are key, claims Nvidia’s edge AI and robotics head.
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While the field of robotics is advancing fast, what’s needed most to accelerate its development, according to Amit Goel, is more humans.
The director of
product management – Edge AI and Robotics at intelligent computing giant Nvidia
adds that there’s a lot more work needed to move the industry forward.
“The tools need to
be easier to use so that developers are not intimidated. It should be that you
don’t need a PhD before building a robot. A lot of entrepreneurial innovation
has to happen for the robot to become more mainstream,” he says.
He compares the
number of developers today working on apps for mobile phones to those working
on robotics. “The numbers are way skewed to the app.”
At the same time
there is rising demand for robotics to plug the widening gap in labour
shortages. This issue is particularly acute in supply chain logistics and
exacerbated during the pandemic when workers were either locked out of
warehouses or fell sick.
It’s also a key
reason why the global robotics market is projected to grow at over a 25% rate
annually, an increase from approximately 20% prior to Covid, to reach $210
billion by 2025, according to Statista.
“The initial
robotics market was predominantly in large scale manufacturing and is now being
adopted by small and medium scale businesses,” informs Goel.
“In automotive
manufacture, robotics were used for doing repetitive tasks in large volume with
little variance but the move into smaller businesses requires more variation
and less volume. That means robots need to be easy to program, deploy,
reprogram and scale.”
That’s where
advances in artificial intelligence (AI), semiconductor technology, mobile edge
computing and more affordable sensor capability come in. Combined, they are
helping to bring robotics and automation, previously considered out of reach of
many markets to users in everything from healthcare to agriculture and food
delivery.
Next gen bot plans
Nvidia made $5bn in
revenue in 2021 mainly from sales of microprocessors but its software products
are strong too. Both hardware and software divisions are targeting robotics.
The goal of its Robotics Research lab based in Seattle is to develop the next generation
of robots that can robustly manipulate the physical world and work alongside
humans.
“Across the board
we see huge growth in robotics and automation fuelled by the fact that there’s
a lot more ecommerce and packages moving through the system,” Goel says. “Also,
so many baby boomers are retiring right now and leaving many jobs unfilled
which is why automation has become critical for some industries.”
For that reason, he
says, robots in the enterprise are not so much eliminating jobs but improving
overall productivity.
“The new wave of
robots are enabling things not possible before. Think about an assembly line.
Before, everything was fixed so you could only manufacture things in a certain
order. Now with AMRs (Autonomous Mobile Robots) you can take a part anywhere
you want, you can change the layout anytime. It enables just-in-time time
configuration that allows you to get more efficiency out of your factory.”
Using AMRs at
industrial scale, even small routing optimisations could save billions of dollars
in the $9 trillion logistics industry. Research firm ABI estimates that
the market for industrial robots will double from 406,000 units in 2021 to
815,000 in 2030, reaching $27bn in value.
Many AMRs in
logistics and warehouses “don’t need that .01mm precision” of more advanced
applications, so the cost of the tech is more affordable.
The lifecycle of
this new generation of AI robots includes some common pieces of technology.
Since all robots run on data the first port of call is to collect data for the
specific application. Nvidia offers Replicator that enables synthetic data
generation.
“Many industries
are not equipped with sufficient sensors and do not have a lot of historic data
to use for training the AI model,” Goel says. “We develop the tools and
platforms that allows you to create large amounts of data and we provide the
entire AI framework to train AI models in the cloud or at the edge all the way
onto the robot.”
Since robotics
development teams tend to be spread across the world, Nvidia’s Omniverse
platform – built for enterprise 3D design collaboration and digital twin
simulation – enables them to work in an online environment capable among other
things of simulating real world physics.
The Isaac Sim
(built on Omniverse) simulates the behaviour of robot fleets, people, and other
machines using digital twins with high-fidelity physics and perception. It
assists in robotics design, verification of sensors, validating performance of
the algorithm and team collaboration.
“There have been
robot sims before but being able to collaborate within the simulation
environment is game changing,” Goel says.
Once trained in a
sim you need to deploy intelligence onto the robot itself using High
Performance Computing. Nvidia’s AI computer Jetson can process up to
275 Trillion Operations per Second (TOPS) for realtime support of multiple
sensors.
“With Jetson we are
bringing server class computing in a small form factor so it can live in a
robot brain,” he explains. “You can connect it to all your cameras and run
computer vision and control.”
Once deployed the
robot will have actual data to learn from. Data can be streamed from the robots
to a digital twin existing in the Omniverse for it and its behaviour to be
continually assessed and moderated, retrained, retested, and redeployed.
Amazon, for
example, uses Nvidia Omniverse and Isaac Sim to simulate
warehouse design, train robot assistants and gain operational efficiencies
before physically implementing them in warehouses. PepsiCo is using
Omniverse to optimise warehouse layouts and workflows to accelerate
throughput before making physical investments or executing changes.
Nvidia runs its own
startup booster program called Inception and aims to reduce the time it takes
for new robots to be built. It recently joined forces with Open Robotics to add
the widely used open-source ROS 2 (Robot Operating System) to Omniverse. The
partnership essentially combines the two most powerful robotics development
environments and the two largest groups of robotics developers.
Nvidia Isaac ROS for Autonomous Mobile Robots
“It’s all very well
to programme a single robot but it needs to exist in an ecosystem cohabited by
other machines and people. That is where the digital twin and Omniverse is
important since we can simulate what happens to a robot in context.”
That’s not to say
there aren’t considerable challenges ahead. Although designed to shore up the
world’s supply chain, the supply chain to actually make and ship robots remains
fragile.
“Customers may be
ready to order but there are just not enough parts available,” says Goel.
“Another factor is that the level of upscaling needed to operate these robots
needs addressing. All robots have a degree of autonomy but they still need to
be managed and supervised by people and that requires new training of the
workforce.”
In addition to
which, the technology itself is still nascent. “We’re just scratching the
surface,” says Goel. “If the long term goal is to make seamless robotic deployment
then among other things we need better algorithms and more intelligent
systems.”
To become more
intelligent and therefore useful the robotics industry needs more compute
capacity to run more sensors and more algorithms to better perceive and understand
the world. The robotics market may reach $210bn in a few years but that is a
fraction of the value of the products and services that will eventually be
generated by its advance.
As Goel says: “We
provide the tools so that your robot can live tens of years of life in a few
days. That is how you accelerate development and make it more affordable.”
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