Narrator: One minute, it's a self-driving miniature car
with a top speed of 14 miles per hour
and a payload capacity of 110 pounds.
The next, it's a two-legged humanoid robot
able to climb staircases and use elevators.
Its origins can be traced back to the Anymal,
a quadrupedal robot
that can autonomously navigate rough terrain.
But the latest iteration being developed
by ETH Zurich's Swiss-Mile company
could eventually be more than just
the most efficient delivery bot on the market.
It could actually save lives.
The Swiss-Mile robot is the brainchild of Marko Bjelonic.
I asked the question, can we improve
the capabilities of these machines by a hundredfold
and not just through a marginal fraction?
So the answer to this question was to add wheels,
which managed to increase both speed and efficiency
by these high fractions.
And actually, this humanoid robot
was something that started out as fun.
We said, shouldn't it be possible
to stand on two legs and then drive around?
And then we thought, OK, this could actually
be something more than just a fun project.
Narrator: The all-electric 6-foot-tall robot
utilizes a combination of 16 motors, GPS,
lidar sensors, and cameras
to autonomously navigate city streets and avoid obstacles.
So, the size is very much governed
by the environments that we build for humans.
And especially for the logistics part,
you need to be able to carry a certain payload,
otherwise you're not very useful.
And then we have the whole aspect
of becoming also environmentally friendlier.
Narrator: Some might wonder how the agile robot
moves without any kinks
when it switches from four wheels to two
or climbs a staircase.
This type of locomotion is taught to its AI control system
with a reinforcement-learning algorithm
that utilizes a reward system
to get what they want from the robot.
Well, you have a robot, and it does a motion.
We evaluate this motion,
and we either punish the robot or give it a reward.
And through this punishment and giving a reward,
the robot tries to optimize for rewards.
So, one of the rewards could be "don't fall,"
or one of the rewards are "go over these stairs."
4,000 robots in parallel
on a GPU-accelerated computer,
which is just a, like, super-beefy PC,
generate maybe data of one and a half years.
And what the robot does,
it has many different instances,
and you refer to, for example, the stairs,
so there's also a model of the stairs
in that simulation environment
where the robot tries to go down the stairs
without falling.
Narrator: Besides being an ideal tool
for last-mile delivery services
and a convenient data-mapping option
for creating digital twins of different environments,
perhaps the most exciting potential future
for the Swiss-Mile robot is on rescue teams.
You want to send the robot in into an unknown environment
to make a first assessment for the rescue team
so that they can go in into a known environment.
For finding lost people in forests
or let's say there's a fire that comes out
on a chemical plant,
I think, if I make a naive prediction,
I think it will happen before 2030.
And for many years,
we already work here in Switzerland
with search-and-rescue teams directly
to really see what is needed
to actually make our robots useful for them.
I think on one side is reasoning
through intelligence
for specific tasks, so that the robot,
even without human guidance,
can make important decisions.
The next aspect will be fleets of robots.
If you want to search [for] a person in a forest,
you don't want to send in one robot.
You would like to send maybe 100 robots in.
Narrator: 2030 is still a ways away,
but it's not hard to get excited
imagining a rescue team of 100 transformer robots.
But is it possible
for them to do more than assist in rescue?
I don't want to take too much away,
because this will come,
but it will be able to carry a person,
and by staying at the same size.