Imagine a scientist
who wants to send a robot
to explore in a faraway place,
a place whose geography
might be completely unknown
and perhaps inhospitable.
Now imagine that instead
of first designing that robot
and sending it off in the hope
that it might be suitable,
instead, she sends
a robot-producing technology
that figures out what kind of robot
is needed once it arrives,
builds it and then enables it
to continue to evolve
to adapt to its new surroundings.
It’s exactly what my collaborators
and I are working on:
a radical new technology
which enables robots to be created,
reproduce and evolve
over long periods of time,
a technology where robot design
and fabrication becomes a task
for machines rather than humans.
Robots are already all around us,
in factories, in hospitals, in our home.
But from an engineer's perspective,
designing a shelf-stacking robot
or a Roomba to clean our home
is relatively straightforward.
We know exactly what they need to do,
and we can imagine the kind of situations
they might find themselves in.
So we design with this in mind.
But what if we want
to send that robot to operate
in a place that we have little
or even no knowledge about?
For example, cleaning up legacy waste
inside a nuclear reactor
where it's unsafe to send humans,
mining for minerals deep in a trench
at the bottom of the ocean,
or exploring a faraway asteroid.
How frustrating would it be
if the human-designed robot,
that had taken years
to get to the asteroid
suddenly found it needed to drill a hole
to collect a sample or clamber up a cliff
but it didn't have the right tools
or the right means of locomotion to do so?
If instead we had a technology
that enabled the robots to be designed
and optimized in situ,
in the environment
in which they need to live and work,
then we could potentially save
years of wasted effort
and produce robots
that are uniquely adapted
to the environments
that they find themselves in.
So to realize this technology,
we've been turning to nature for help.
All around us,
we see examples of biological species
that have evolved smart adaptations
that enable them to thrive
in a given environment.
For example, in the Cuban rainforest,
we find vines that have evolved leaves
that are shaped like
human-designed satellite dishes.
These leaves direct bats to their flowers
by amplifying the signals
that the bats send out,
therefore, improving pollination.
What if we could create
an artificial version of evolution
that would enable robots
to evolve in a similar manner
as biological organisms?
I'm not talking about biomimicry,
a technology which simply copies
what's observed in nature.
What we're hoping to harness
is the creativity of evolution,
to discover designs
that are not observed here on Earth,
the human engineer
might not have thought of
or even be capable of conceiving.
In theory,
this evolutionary design technology
could operate completely autonomously
in a faraway place.
But equally it could be guided by humans.
Just as we breed plants for qualities
such as drought resistance or taste,
the human robot breeder could guide
artificial evolution to producing robots
with specific qualities.
For example,
the ability to squeeze
through a narrow gap
or perhaps operate at low energy.
This idea of artificial evolution
imitating biological evolution
using a computer program
to breed better and better solutions
to problems over time
isn't actually new.
In fact, artificial evolution,
algorithms operating inside a computer,
have been used to design everything
from tables to turbine blades.
Back in 2006,
NASA even sent a satellite into space
with a communication antenna
that had been designed
by artificial evolution.
But evolving robots
is actually much harder
than evolving passive objects
such as tables,
because robots need brains
as well as bodies
in order to make sense of the information
in the world around them
and translate that
into appropriate behaviors.
So how do we do it?
Surprisingly, evolution only needs
three ingredients:
a population of individuals which
exhibit some physical variations;
a method of reproduction
in which offspring inherit
some traits from their parents
and occasionally acquire
new ones via mutation;
and finally, a means of natural selection.
So we can replicate these three
ingredients to evolve robots
using a mixture of hardware and software.
The first task is to design
a digital version of DNA.
That is a digital blueprint that describes
the robot's brain, its body,
its sensory mechanisms
and its means of locomotion.
Using a randomly generated
set of these blueprints,
we can create an initial population
of 10 or more robots
to kick-start this evolutionary process.
We've designed a technology
that can take the digital blueprint
and turn it into a physical robot
without any need for human assistance.
For example, it uses a 3D printer
to print the skeleton of the robot
and then an automated assembly arm
like you might find in a factory
to add any electronics and moving parts,
including a small computer
that acts as a brain.
And to enable this brain to adapt
to the new body of the robot,
we send every robot produced
to an equivalent of a kindergarten,
a place where the newborn robot
can refine its motor skills
almost like a small child would.
To mimic natural selection,
we score these robots
on the ability to conduct a task.
And then we use these scores
to selectively decide
which robots get to reproduce.
The reproduction mechanism
mixes the digital DNA
of the chosen parent robots
to create a new blueprint
for a child robot
that inherits some of the
characteristics from its parents
but occasionally also
exhibits some new ones.
And by repeating the cycle of selection
and reproduction over and over again,
we hope that we can breed
successive generations of robots
where, just like is often observed
in biological evolution,
each generation gets better than the last,
with the robots gradually optimizing
their form and their behavior
to the task and the environment
that they find themselves in.
Now, although this can all take place
in a time frame that's much faster
than biological evolution,
which sometimes takes thousands of years,
it's still relatively slow in terms
of the time frames we might expect
in our modern world
to design and produce an artifact.
It's mainly due
to the 3D printing process,
which can take more
than four hours per robot,
depending on the complexity
and the shape of the robot.
But we can give our artificial
evolutionary process a helping hand
to reduce the number of physical robots
that we actually need to make.
We create a digital copy
of every robot produced
inside a simulation in a computer,
and we allow this virtual
population of robots to evolve.
Now it's quite likely that the simulation
isn't a very accurate representation
of the real world.
But it has an advantage that it enables
models of robots to be created
and tested in seconds rather than hours.
So using the simulator technology,
we can quickly explore the potential
of a wide range of robot types
of different shapes and sizes,
of different sensory configurations,
and quickly get a rough estimate
of how useful each robot may be
before we physically make it.
And we predict that by allowing
a novel form of breeding
in which a physical robot can breed
with one of its virtual cousins,
then the useful traits
that have been discovered in simulation
will quickly spread into
the physical robot population,
where they can be further refined in situ.
It might sound like science fiction,
but actually there's a serious point.
While we expect the technology
that I've just described
to be useful in designing robots,
for example, to work in situations
where it's unsafe to send humans
or to help us pursue our scientific quest
for exoplanetary exploration,
there are some more pragmatic reasons
why we should consider
artificial evolution.
As climate change gathers pace,
it is clear that we need a radical rethink
to our approach to robotic
design here on Earth
in order to reduce
that ecological footprint.
For example,
creating new designs of robot
built from sustainable materials
that operate at low energy,
that are repairable and recyclable.
It's quite likely that this
new generation of robots
won't look anything like the robots
that we see around us today,
but that's exactly why
artificial evolution might help.
Discovering novel designs by processes
that are unfettered by the constraints
that our own understanding
of engineering science
imposes on the design process.
Thank you.
(Applause)