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PHYSICAL INTELLIGENCE IS BUILDING A BRAIN FOR ROBOTS

Physical Intelligence is building software intended to power robots that can
learn a wide range of tasks.

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Instead of focusing on specific types of robotic arms or industrial androids,
Physical Intelligence wants to develop software that can be applied across many
types of robotics. 

Photographer: Tunvarat Pruksachat/Moment RF
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By Ashlee Vance
March 12, 2024 at 8:26 PM GMT+1
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5:18

Since the earliest sci-fi books and films, computers imbued with artificial
intelligence have almost always been accompanied by equally clever moving
machines, like androids and other robots. Over the past 15 years or so, however,
AI systems that work entirely in software have grown far more sophisticated than
their moving counterparts. Robots can build cars in factories and clean up after
us at home but are able to carry out a relatively small range of tasks compared
to the increasingly general nature of chatbots.

A startup called Physical Intelligence has set out to alter this situation.
Formed this year by a team of robotics and AI experts, the company plans to
create software that can add high-level intelligence to a wide variety of robots
and machines. Or, as co-founder and Chief Executive Officer Karol Hausman puts
it in Physical Intelligence’s first public interview since founding the company:
“We aim to bring AI to the physical world with a universal model that can power
any robot or any physical device basically for any application.”



The rise of the language processing AI built by OpenAI, Google and others have
been made possible by the huge volume of text available on the internet and in
other archives. Companies can train their AI models by feeding them billions of
examples of how humans use words, a process that has helped computers to
mathematically “solve” language. Gathering similar amounts of data from the
physical world has proved far more challenging, limiting the progress of AI in
the robotics field.

Physical Intelligence’s thesis is that the time is right for a new approach to
building robotics AI models. The company looks to merge the techniques used to
build language models with its own techniques for controlling and instructing
machines. The end goal would be to create an AI that works as a type of general
purpose robotics system.

Hausman spent the last few years as a scientist working on robotics at Google.
His fellow co-founders include Sergey Levine, who has done pioneering robotics
work as a professor at the University of California, Berkeley; Chelsea Finn, a
professor at Stanford University; Brian Ichter, a former Google research
scientist; and Lachy Groom, a former executive at the payments company Stripe
and prominent tech investor. Their company has raised $70 million from Thrive
Capital, OpenAI, Sequoia Capital, Greenoaks Capital Partners, Lux Capital and
Khosla Ventures, according to a person familiar with their investors who was not
authorized to speak publicly about the investment.

Efforts to improve the software that powers robots have been going on for
decades. Notably, a company called Willow Garage, formed in 2006, spent several
years trying to build a type of general purpose software that could be shared
across robots and give them a unified set of basic functions. While its software
was picked up by a number of companies and robotics developers, Willow Garage’s
work did not lead to a huge a leap forward in robotic intelligence, and the
company wound down its operations in 2014.



Other companies like Rethink Robotics tried to build systems that could learn to
do jobs by copying movements shown to them by humans. A number of startups have
recently implemented AI that uses repetition to teach robotic arms to pick up
objects and perform tasks similar to those done by humans in warehouses. Others
have begun building androids designed to mimic human movements. One of these
startups, Figure AI Inc., just raised $675 million from OpenAI, Nvidia, Jeff
Bezos and Microsoft to help it build robots to work in logistics and
manufacturing facilities.

Instead of focusing on specific types of robotic arms or industrial androids,
Physical Intelligence wants to develop software that can be applied across many
types of robotics. To do this, it has set to work creating its own AI model
designed to bring basic human abilities to machines. “I think it’s really cool
what people are building with humanoids,” Groom says. “But what fundamentally
makes humans interesting is the brain, not our hardware. We are the ultimate
generalists.”



Over the past few years, researchers have published a string of papers that show
how robots are already gaining new levels of intelligence from language and
image AI models. If, for example, you tell a robotic arm to tie a shoe, it can
use AI to find the basic concepts of a shoe and shoe laces and information about
what tying a shoe usually entails. While this is a good start, the robotics
hardware still needs some level of training to actually accomplish physical
tasks. This has been a major challenge, because having robots perform tasks
enough times to learn each new job is time-consuming and costly.

Physical Intelligence has yet to reveal exactly how it plans to overcome this
problem. The company’s co-founders say they will not build their own hardware
but rather purchase a variety of different robots and train their AI models on
that hardware. The objective is to amass the largest body of robotics data
created to date, according to the company.



Physical Intelligence faces competition from companies like Figure AI and Tesla
Inc. that are making androids, as well as other companies working on general
purpose robotics software. Earlier this week, for example, the seven-year-old
startup Covariant said that it too has been developing a new type of robotics
model that plays off the language, image and video AI advances to improve the
abilities of its robotic arms.

The co-founders of Physical Intelligence maintain that they’ve been working
through the challenges of AI for robotics for years and have developed novel
approaches to issues that have slowed others in the field. “Realistically, I
think we are going to need a long and very serious research effort to make this
happen,” says Levine. “But there are enough signs that the biggest obstacles to
use robots in the real world are now solvable.”


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