February 20, 2025 • 45 mins
Jeff Cardenas is the co-founder and CEO of Apptronik. Jeff's problem is this: Can you make a safe, reliable humanoid robot – for less than $50,000?
In the short term, Apptronik’s robots will work in factories. But Jeff’s long-term goal – based on the experience of his own grandparents – is to build robots that can help care for the elderly.
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Episode Transcript
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Speaker 1 (00:15):
Pushkin As a general matter, I'm a fan of technological progress,
but I'll admit that humanoid robots kind of creep me
out for you know, obvious, normy, uncanny Valley type reasons.
And yet there is an exchange that you'll hear near
(00:36):
the end of today's show. That's the most compelling argument
I've ever heard for humanoid robots. And it's not just
an intellectual argument, it's an emotional argument. If that's a phrase,
it's really a very human argument for humanoid robots. I'm
(00:59):
Jacob Goldstein, and this is what's your problem to show
where I talk to people who are trying to make
technological progress. My guest today is Jeff Cardinas. He's the
k founder and CEO of Eptronic. Jeff's problem is this,
can you make a safe, reliable humanoid robot for less
than fifty thousand dollars. We started our conversation talking about
(01:21):
the DARPA Robotics Challenge. DARPA, of course, is the government
agency that helped to create the Internet and that gave
a big push to early self driving cars, among other things.
And Jeff says the agency's robotics challenge, which happened a
decade ago. Happened in twenty fifteen played a key role
in launching a bunch of the companies that are now
(01:42):
working on humanoid robots.
Speaker 2 (01:46):
The Darker Robotics Challenge was a challenge that was created
in the wake of the Fukushima disaster. Lukashima happened in
the you know, there was a meltdown in the nuclear
reactors and it was unsafe for people to go in,
and in essentially what happened was they needed a robot
to go in to sort of prevent a broader catastrophe.
(02:08):
And as they went out to the robotics community, the
idea was calling all roboticists, we need a robot to
go in and to help out here. And there was
no robots that could do all the different tasks that
were required to actually get to the melted down reactors.
So you had to go down steps, you had to
open doors, had to do a whole range of things.
(02:29):
And in the wake of that, basically what DARPA said was, certainly,
there's got to be the technology to enable us to
have much more versatile robots for natural disaster relief so
this never happens again. So out of that, Darker created
something called the Darker Robotics Challenge, and so there's a
variety of teams around the world that were put together
(02:51):
to build these general purpose robots. And the team that
we came out of was the Nasagen and the Space
Center team working on a robot called Valkyrie.
Speaker 1 (03:03):
So I want to go back to this moment when
the DARPA Challenge and there's this big final test, and like,
what we have from it is not some incredible breakthrough,
but like a blooper reel of robots, what are they doing?
Falling downstairs or driving cars into walls or.
Speaker 2 (03:21):
Something pretty much. I mean, the blooper reels make it
seem worse than it was, but but yeah, we had
we basically the realization was the technology's not there. It's
going to take time to continue to mature until it
can get to the point where it's actually commercially viable.
Speaker 1 (03:40):
And so it's interesting, it's super interesting right that this
moment is not the like beginning of some humanoid robot winter,
but rather the beginning of this humanoid robot industry. Like
how does that work? Like how do people whatever start
companiesn't get money out of this seemingly disappointing moment.
Speaker 2 (04:00):
Well, I think it actually was in winter, you know,
when we started in you know, the company was incorporated
in twenty fifteen and we started in twenty sixteen, and
for the most part, nobody wanted to talk about humanoids
and nobody was really paying attention to it. There was
a handful of folks that I sort of think of
as the true believers that were really working on this
(04:21):
problem and thought, you know, we don't care how long
this is going to take. We're just going to keep
working on this no matter what. But for the most part,
the entire robotics industry was very anti humanoids coming out
of the Darker Robotics Challenge, and in fact, there was
many people that were saying they'll never be viable, like
why would you ever use a humanoid robot. They're too complicated,
(04:42):
they're way too expensive, and you'll always use a simpler robot.
So actually, most of the people that we met when
we decided to start Uptronic were doubters, and we're saying,
humanoids will never make sense. We'll use these special purpose robots.
You know, maybe in fifty years humanoids will make sense,
but not for a long time.
Speaker 1 (05:02):
I mean special purpose robots is a pretty compelling case, right,
like whatever we all have, you know, dishwashing robots and
clothes cleaning robots in our houses, and like, you know,
wheels seem way easier than legs for lots of things.
And obviously there have been robot arms for what I
don't know, seventy years now or something like. So robots
(05:25):
in a way are all around us. Why would you
build a machine that looks like a dude when that's
like wildly hard.
Speaker 2 (05:30):
Right, Yeah, I mean I was naive coming out of
graduate school, and to me it seemed obvious. And the
way I used to think about it was you could
either have thousands of robots that do one thing, or
you could build one robot that could do thousands of
different things. And when I would talk about this with Nick,
my co founder, Nick would say, look, you can either,
(05:51):
you know, invest all this engineering in each of these
sort of narrow solutions, or yes, a humanoid robot, a
viable humanoid robot could take you years, it could take
you a decade, But once you invest all this time
in this single platform, then you can reap the benefits
of that across You can spread the research of that
(06:13):
across many different applications.
Speaker 1 (06:15):
I mean, is there not a middle case where there's
like some core kind of functionality that you develop that
works across many different types of robots. Is that a
less straw many version of the non humanoid robot kind
of argument?
Speaker 2 (06:29):
I think there could be. But I came into robotics
and basically just saw a lot of challenges with the
business models. So you build this special purpose robot, you
custom program the robot. In the industrial space, you can
spend six times the price of the robot on just
systems integration, and then the robot just does one thing.
(06:50):
So this idea that you could have a much more
versatile robot, to me, seemed obvious, like, if robotics is
going to scale, we have to have much more versatile
robots that we've had in the past. So if you
sort of think of that as the premise is we
need more versatile robots, then the question is, well, how
do you get there and what does versatility mean? And
that's where it led me to the humanoid making a
(07:12):
lot of sense, because if you have to modify the
environment for every new task that the robot can do,
you run into the same problem that we had in
sort of classical robotics. But if the robot can retrofit
into the environment such that you don't have to change
or modify the environment for every new task that the
robot can do, then it seemed to me that this
(07:33):
would maybe be the key unlock for robotics to actually
scale to the masses.
Speaker 1 (07:38):
The demand would be infinite. If you had a thing
that was the size and shape of a person with
arms and legs like, scale would be off the charts,
And presumably that's what drives costs down, and that's like
the good flywheel.
Speaker 2 (07:50):
Right, yeah, exactly.
Speaker 1 (07:52):
Okay, so you had this big idea about humanoid robots
and you started a company. But at the moment you
started the humanoid robot company, the prevailing sentiment was like,
deeply skeptical, what happened?
Speaker 2 (08:03):
What did you do? Well? A handful of us kept
working on it, so I didn't know any better. You know,
sometimes it's better that you don't know any better. I
thought humanoids were really cool, and I thought that it
just seemed it made sense to me that, you know,
how are we going to get to millions of robots
that are working and you know, with and around humans
(08:24):
and all these environments, and to me, this seemed like
the only way that that was going to happen. And
the way I looked at it was, even if we failed,
this was a worthy pursuit and I would be proud
that I tried to do it. And so the way
that we did it was we bootstrapped the company. There
was no investors that were willing to invest in humanoid
robots at the time that we got started, especially for hardware,
(08:48):
which we can talk about that as we move forward.
And so we bootstrapped the company and we basically got
paid to build robots for a lot of different folks.
And for the first five years of the company, we
just built the company on revenue. We would get project
after project and somehow never died.
Speaker 1 (09:08):
Like what kind of were you taking at that time?
What's an one example, Well, we.
Speaker 2 (09:12):
Had our first contract was with NASA, So we had
a contract with NASA to build Valkyrie two, to take
the lessons learned from the DARPA Challenge and build the
next iteration of Valkyrie. We were really kind of pioneering
new ways of building these systems. So US Special Forces
ended up coming to us about a year in and said, hey,
we want to do Ironman suits, and our view was
(09:33):
this was kind of a humanoid robot that you wear.
We worked in automotive. We helped build humanoid robots for
a couple you know, major companies that are still working
on these things today, and we would help sort of
pioneer new ways of building their platforms. So we've done
fifteen unique robots since we got started, and we're now
(09:54):
in a ninth iteration of humanoid and I have only
raised money in the last couple of years.
Speaker 1 (09:59):
So where'd the idea to build a robot for fifty
thousand dollars come from?
Speaker 2 (10:08):
The idea of where to build a robot for fifty
thousand dollars was what will it take for these robots
to be economic and reach mass market? So, you know,
when we got started, sort of my view was, you know,
what will a truly viable commercial humanoid look like and
what would the bomb costs need to be for this
to make sense? And if you sort of just do that,
(10:30):
bottoms up about fifty thousand dollars for the robot, you're
in the money for mass market. You can still do
some tasks in a very economic way at even one
hundred thousand or one hundred and fifty thousand, but fifty
thousand was the goal. This has now been blown by
by some of the new entrepreneurs that are coming out
that are talking about sub twenty thousand dollars. But it
(10:52):
never made sense to me that robots were as expensive
as they were at the time. If you look at
a humanoid compared to a car, there's about four percent
the raw material by weight. So one of our robots
there's about three hundred dollars of raw aluminum, which is
the the base metal of the system. And so it
(11:14):
never made sense to me that these robots would need
to be any more than fifty thousand dollars. As you
could reach scale and as you can start to think
about new ways of building them in similar ways that
we build other machines.
Speaker 1 (11:26):
So you decide you want to build a fifty thousand
dollars robot, Like, what do you actually do to do that? Like,
how do you go from having an idea of building
a fifty thousand dollars robot to having a fifty thousand
dollars robot.
Speaker 2 (11:40):
Well, you iterate until you solve any problem. So what
we would do is basically we would get a project
or a contract to build a robot, and we would
put a lot of different ideas into those designs. In
early days, it was all about performance. How can you
get the robot to just do these tasks, to stand
(12:01):
and have a battery life that's long enough. And then
as we kept evolving, we started to really focus on
cost in addition and scalability and assemblability and robustness. And
the key building block to drive cost and performance is
the actuator. So I mentioned we've done nine iterations of humanoids,
but we've done sixty iterations of electric actuators.
Speaker 1 (12:24):
Actuators are basically the thing that makes the robot.
Speaker 2 (12:27):
Move, right, Yeah, the muscles of a robot.
Speaker 1 (12:31):
So where are you now? Tell me about Apollo.
Speaker 2 (12:34):
Yeah, we're now at an exciting point. We have about
one hundred and seventy employees at Electronic. We are piloting
these robots right now. So I think the entire industry
is still in the pilot stage overall. There's some commercial
orders that are happening, but still early days for humanoids.
We're working with a handful of really great partners, folks
(12:55):
like Mercedes and GXO, and we're getting the robots out
into the real world and in pretty big ways, and
so we'll have more announcements over this year. We have
a big partnership with Google Deep Mind, which is something
that I always dreamed of coming out of graduate school.
We had a lot of respect for the folks that
at Google, and they have a whole history and legacy
(13:17):
in the humanoid space as well. And basically right now
we're getting these robots out into the world and gearing
up for you know, real commercialization, which we expect to
happen in twenty twenty six.
Speaker 1 (13:32):
What's the robot look like?
Speaker 2 (13:33):
The robot kind of looks like a superhero. Maybe this
has been kind of the idea that we've had from
the beginning. It's got two eyes and a face. It's
five foot eight, weighs one hundred and sixty pounds, has
four hour swappable batteries. Yeah, it's got a screen on
its chest and a face and that's about it.
Speaker 1 (13:53):
Two arms, two legs. What's to have in the way
of hands.
Speaker 2 (14:00):
So it has hands, has five fingered hands. You know,
there's these debates that I think of his false debates
in the humanoid space. So a lot of people when
they sort of knock humanoids and the viability of humanoids.
It usually has to do with do they need legs
and do they need hands? And the answer to that
question for me is no, they don't. It's a robot,
and robots are modular. So we can put Apollo on
(14:21):
any mobility base. We can put it on wheels, we
could put it on tracks, we could stationary amount the
upper torso of Apollo. And the same thing's true for
the hands of the grippers. We can use parallel grippers,
or we can use five fingered hands.
Speaker 1 (14:37):
Hands are like a whole thing, right, Like hands are?
Is it partly because they're so hard? Like what's going
on with robots and hands?
Speaker 2 (14:44):
Yeah, it turns out hands are a whole thing. This
is another one of those things that you know. It's
it's almost better that you don't understand the complexity before
you get into it, or else you might not have
done it in the first place. Ninety eight percent of
all tasks that humans do are done with our hands.
So there are narrow things that humanoids can do without
(15:06):
more dexterity, but it's very limited relative to the whole
sort of you know, all the different types of tasks
that humans do. Most of the things we do involve
our hands, and certainly in the industrial space, most of
the work is done with hands, So solving the end
defector or the hand problem is a big deal. There's
a lot of different debates about what you need and
(15:28):
how you get something that can actually perform industrial work.
You know, we've chosen the five finger hand route and
we're working across the space to really make some big
advancements there.
Speaker 1 (15:41):
Overall, it's part of the trade off, Like I could
build whatever two what do you call them prongs? Like
if you had two fingers, basically like a claw, Like
you could do a lot of things with a claw.
Presumably it would be way easier, but you couldn't do everything.
Is it kind of like what are you optimizing for?
And sort of how much payoff now versus how much
payoff later?
Speaker 2 (16:01):
Yeah, I think that's exactly right. It's you know, versatility,
you know, compared to robustness and cost. Basically, how much
complexity do you want to have on the system. And
you know, for these robots to be really viable in
the long run, especially in the industrial space, they've got
to be able to operate two shifts a day minimum.
(16:22):
Really you know, twenty two hours a day, seven days
a week. But solving that problem in a hand, so
just getting the performance of the hand first, but then
the robustness for them to do that type of work
is the next piece. And that's a trade off of
performance and complexity and cost.
Speaker 1 (16:39):
Because like it gets delicate, right, presumably the fingers so
to speak, would be fragile, right, Yeah, they can bring
easy to break.
Speaker 2 (16:47):
Yeah, yeah, yeah, and you've got to maintain it and
you've got to support those systems and fix them out
in the field. And so what's the trade off there?
And that's a whole trade space that we've been working
on over a long time.
Speaker 1 (17:00):
So we've been talking about hardware. Let's talk about the
software side. What's happening with that.
Speaker 2 (17:08):
A lot's happening on that side. I think we're really
in a really you know, we're an exciting point for
robotics overall. Think of the AI as really the last
piece of the puzzle. So, you know, we've had the
ability to build complex robots for a relatively long time.
We're just now, you know, really figuring out how to
(17:28):
take the lessons from automotive and consumer electronics and and
build much more economic systems, and we've had some advancements
and things like motors and batteries and compute and sensors
that have all sort of built up to this moment.
But the final piece of the puzzle was the AI
and the intelligence and essentially the way to think about it.
(17:48):
And I think Jensen does a great job of explaining this.
Speaker 1 (17:50):
But the advances Jensen Wong from Nvidio, Yeah, that's right.
I feel like Jensen is not quite the Elon level
of one name, household name, but sorry, go on, he's
getting there.
Speaker 2 (18:02):
Yeah, he should be.
Speaker 1 (18:04):
He should be. He should be.
Speaker 2 (18:07):
Basically, the advancements in geni AI turn out to apply
very well to robotics, and particularly to humanoid robotics, So
you can basically map human movement and trajectories from humans
doing things and build big data sets and use that
to train robots to do similar tasks in similar environments.
And these transformer architectures that we're using and generative AI
(18:31):
actually apply very well to robotics, and so this has
been a big sort of breakthrough moment for robotics. And
so I think as an industry as a whole, everybody's
really excited right now because we're reaching new heights and
we're able to do things that we dreamed about doing
with robots only you know, even a few years ago,
are now possible, and we're seeing a really rapid advancement
(18:53):
in performance overall.
Speaker 1 (18:54):
What's an example of a thing that you could only
dream of a few years ago that robots can do now?
Speaker 2 (19:00):
I think it's more dexterity and versatility. So just the
range of things that you can do. So the challenge
for robotics was that each news even if you build
something like a humanoid robot, and this is true for us,
and say you build an application to pick boxes off
of a palette and place those boxes onto a conveyor. Well,
(19:21):
you hand build that application, and you know, maybe takes
you eighteen months to sort of ring that out and
get it to a certain amount of robustness. Well, now
you want to do the reverse of that and pick
off of a conveyor and palletize something that will take
you the same amount of time that it took you
to build the initial application.
Speaker 1 (19:41):
You have to write basically have to write a whole
other piece of software. You have to start from scratch
almost yeah.
Speaker 2 (19:47):
Yeah, exactly. And so basically what is happening now is
that we now have these much more sort of general
models that where you can collect a lot of data
at the top layer, and so each new task that
you want to perform actually takes less and less incremental
amount of work. So what it's opening up now is
(20:08):
more dexterous applications.
Speaker 1 (20:13):
Still to come. On the show how Jeff's grandparents inspired
his work on robots, So you were talking about using
the transformer model. That has been the you know, breakthrough
that has driven large language models in training robots essentially.
(20:38):
I mean, of course, a key sort of serendipitous thing
that happened with language models was there is this crazy
large data set of words and pictures which is the Internet,
and there's not an analogous data set for the physical world. Right, Yeah,
it seems like that is is that the rate limiting step?
(20:58):
Is that the big problem in sort of AI for robots?
Speaker 2 (21:03):
Yeah, I mean there's a lot of work that's still
happening at the research level for you know, how can
you pull that kind of data from from videos so
you can think of big data sets of humans doing
things that could be really interesting to train robots in
the future. Interest and that that will come into play
over time. But yeah, it's the chicken or the egg problem.
(21:23):
And data is the one of the key things that
we need to enable the next wave of breakthroughs. And
and this is kind of the race, is can you
get robots out into the to the real world, into
the field and collecting data very high you know, in
very high volumes. Whoever does that, you know, will we'll
have better models. And this is the data flywheel. So
(21:46):
this is kind of the race that's on right now
where you hear a lot of other humanoid CEOs talking
about getting a lot of robots out into the world.
Largely those are going to be under Teley operation collecting
data and then you know, training and building these models
of the future.
Speaker 1 (22:01):
Ah So so it's like whoever gets there first will win,
just because that'll be the accelerant. Like once you have
robots in the world and you're collecting data, then you're
immediately getting ahead of whoever has fewer robots out in
the world because they're collecting less data.
Speaker 2 (22:16):
Yeah, so.
Speaker 1 (22:18):
Tell me about Telly operation.
Speaker 2 (22:21):
Tellyoperation is basically just remotely controlling the robot, so you're
taking over the robot. You can see through the robot's
eyes with a VR headset, and then you're controlling the
robots arms and hands to do a particular task. It's
like a video game and you're controlling a robot. It's
the simple idea. There's a couple of reasons it's important.
(22:44):
The first thing is that it tells you what the
robot's physically capable of doing. So if I'm completely controlling
the robot and I can't do a task under Telly operation,
then that means the robot's not physically capable of doing
it to be very difficult for an AI control system
to control the robot to do that. So this is
how we understand the physical capabilities of the robot as
(23:05):
these new models have come along. The simple idea is
that if you can tell you operate the robot to
do a task, then you should be able to automate
that task on the other end. So if you can
collect enough data under Telly operation, then you can automate
it by running it through these similar architectures that we
talked about.
Speaker 1 (23:25):
And so's the basic idea that like you use remote
control to like drive the robot to do a thing
whatever A thousand times some number of times, and in
doing that, you're training the robot, you're training the software
training the I.
Speaker 2 (23:40):
Yeah, that's exactly right.
Speaker 1 (23:42):
What's an example of a thing that you've done that way?
And like, you know, how many times did you have
to remote control it before the robot could do it?
Speaker 2 (23:48):
So each is picking is a good example. Or you
know you're taking objects and you're putting them into a
box to do that in a simple context, thousands of
demonstrations is what you need, you know, and we think
of this as generally ours. So you know, how many
hours of data collection have we done, and thousands of
(24:11):
iterations can get you to let's say, eighty percent of
human rate. If you want to get to ninety five
percent or better of human rate, then you need more
and more data. But it's in the thousands, it's not millions.
Speaker 1 (24:24):
Yeah, thousands makes it seem totally tractable.
Speaker 2 (24:27):
Yeah, that was actually surprised by how well these models
work and actually how little data they need to get
relatively good performance. And you're seeing a lot of demonstrations
of this out there today.
Speaker 1 (24:40):
And presumably that'll get better and better. Right as the
software side of AI gets better and better, it'll learn
faster essentially and the other obvious thing, but I'm just
going to say it is like, once you have done
it once, then it works for every robot. Then you
can make a million robots and they all know how
to do the thing, right.
Speaker 2 (24:57):
Yeah, that's exactly right. And one of the interesting things
about these models is actually the diversity of data is
almost more important than task specific data. So you want
to go wide across a range of tasks, and then
you're basically building all these skills into the robot, and
then it becomes better at doing any one particular task.
Speaker 1 (25:17):
It really is like learning.
Speaker 2 (25:19):
It really is a human esque Yeah, that's right.
Speaker 1 (25:22):
So I know you're in a few pilot projects with
Mercedes and what is GXO big logistics company. When do
you want to start selling robots for real? Like when
do you think that might happen?
Speaker 2 (25:33):
Twenty twenty six?
Speaker 1 (25:35):
Okay, yeah, suddenly that's next year. Almost.
Speaker 2 (25:39):
Now, a year is a long time in you know,
these are dog years. It's a long time in this space.
Speaker 1 (25:48):
And twenty twenty six could be almost two years.
Speaker 2 (25:50):
Yeah.
Speaker 1 (25:51):
Now, like who are you going to sell robots to and
how much you're going to charge and what are they
going to.
Speaker 2 (25:57):
Do so the initially in manufacturing and logistics, so folks
like Mercedes and GXO, these are the initial customers of
these systems. We are not announcing pricing yet, but you
can think of it as you know, take what you
know it costs to do these tasks today and with
(26:17):
some discount to what it costs to do these tasks today.
We have a rass model that we that we use,
so you basically robot as a service. Yeah, robot as
a service model where you're you're paying the robot basically,
you know, by the hour effectively to do a particular task,
and that's at a discount to what it costs to
(26:38):
do that task today.
Speaker 1 (26:40):
How far are you from the fifty thousand dollars robot.
Speaker 2 (26:43):
We're not there yet, so not very far. So we
have the architecture to be able to do this. So
getting the cost down on these robots is a two
step process. So first step is new architectures. So if
you still require this very high precision in the system
and you're using bespoke components that are only used for robotics,
(27:05):
these robots will still be expensive. The challenge of umanoid
robots is they have a lot more motors than traditional robots.
So traditional robot has six or seven motors, a humanoid
robot has thirty to forty plus.
Speaker 1 (27:19):
Okay, so that means it's expensive where you've got to
figure out how to get cheaper actuator.
Speaker 2 (27:25):
Yeah, so we're there. So for us, that was a
five hundred dollars actuator that we and we have a
five hundred dollars actuator now today. And so once you
solve that problem, and once you solve the architecture problem,
now it's about scale and manufacturing. So a lot of
where we spend, a lot of where the cost is
driven at low volumes is in just the structures of
(27:45):
the robot or we're seeing seeing we're milling at of
big blocks of metal parts and very small quantities. But
there's other techniques that are are much more cost effective,
like casting or stamping, and these will allow these robots
to be much cheaper. As I mentioned, look at automotive
(28:06):
and look at the scale of automotive, there's four percent
the raw material by weight and a humanoid robot as
compared to a car. So as you are once you
solve the architecture problems such that you can build a
lot of these systems, and you are, they're simpler to
make than The next piece is just applying mass manufacturing
approaches to this to make them a lot cheaper. As
(28:29):
you skin, well, I mean.
Speaker 1 (28:33):
That's a hard leap to make, right, Like what do
you do? You get a ton of capital and just
build a factory and hope there's demand on the other end, Like,
how do you go from this bespoke expensive thing to
a mass produced, you know, much less expensive thing.
Speaker 2 (28:48):
Well, it's a gradient. So, like I said, step one
is you have new approaches that allow you to make
them cheaper, just inherently on a unit to unit basis.
So the early humanoids were like millions of dollars and
now we're in the hundreds of thousands of dollars range
for building one.
Speaker 1 (29:04):
So you just got to get one more order of
magnitude out of it.
Speaker 2 (29:08):
Yes, they've already We've drop the price by an order
of magnitude. And then now as we build more, and
even as you go as you add a zero, as
you go from ten to one hundred, the price drops
pretty dramatically. So you don't need the volume that you
that you might think, like my view is we can
get to the sub fifty thousand dollars price point in
the thousands of unit quantity, so without without hundreds of
(29:31):
thousands or millions of.
Speaker 1 (29:32):
These So one big buyer, one one big car company
or logistics company might place an order of thousands of units.
Speaker 2 (29:39):
Right, Yeah, And you made a comment that you said,
and you hope that there's demand. One of the things
that I think is important to note is the demand
for these robots is enormous. We have demand for hundreds
of thousands of units already today with the customers that
we're working with. So the demand is enormous. So we're
(30:01):
we're ramping up. You know, we've got to get the
robustness and the safety of the system and really bring
out the design. And you know, we're these are you know,
really credible, thoughtful people that are coming from other industries
that are now joining us, that now see that we've
crossed this threshold of technical viability and now taking lessons
(30:23):
from you know, how you scale and manufacture other things
and bringing that into the robotic space and the humanoid
space overall.
Speaker 1 (30:31):
So in a year or at least next year, you
want to be selling robots for real? Where do you
want to be in five, say years.
Speaker 2 (30:41):
My view is that where this evolves is it's going
to start in logistics and manufacturing, and then as we
solve safety as an industry. I'm really interested in healthcare
and particularly in elder care over time, so you know,
how can these robots be used to improve the way
we live and work. That was really the lens that
I came into this on, and so I think over
(31:04):
the next five years you'll start to see the early
stages of the next three years just to see early
applications for robots entering the home. There's some folks that
are really working hard on this. I think we're about
three years out from that being really viable, but I
hope people prove me wrong. I hope it's faster than that.
Speaker 1 (31:24):
Three years seems fast. What's the sort of first first
use case, first job you imagine a robot doing for
real in somebody's house in three years.
Speaker 2 (31:35):
Well, everybody wants laundry. If everybody I talked to says,
when is this thing going to do my laundry? And
I want that as well.
Speaker 1 (31:42):
There's literally already a machine to do your laundry. All
you have to do is put it in one machine
and then put it in another The remaining work is trivial.
Speaker 2 (31:50):
Yeah, I mean, look, I'm not the person to talk
about the home. I think we're still a ways out.
But there's humanoid companies like one X that are really
focused on on the home, and we've got a lot
of respect for what they're doing over there, and so
I hope they do it. I know that they're working
hard on it. And you know where I want a
(32:12):
robot for the home as well, So you a lot
of the things that are that are happening. And with
these models that I talked about, these more generic models,
the things that we're learning in the industrial base can
apply to the home over time as well.
Speaker 1 (32:25):
In terms of the AI models, sure, I mean the
AA models are basically teaching a robot how to how
to deal with the physical world, that's right, how to
move around, how to pick things up, how to put
things down.
Speaker 2 (32:35):
Yeah, the task and the I mean the home's tough
because like, how much is it even a robot does
your your dishes, your laundry, cleans and cooks for you.
How much are you willing to pay for that on
a yearly basis?
Speaker 1 (32:47):
I'm imagining the first household tasks. I would have thought
you would have said, like people who are quadriplegic, right, Like,
there are a lot of people who have various kinds
of mobility problems who can't do very basic things around
the house, where essentially a robot could do it for them,
Like I would think that would be the first use case.
Speaker 2 (33:03):
I think that's a great use case. And you know,
for me, that's that's sort of in the realm of
what I say is older care, which is like assistive
robots that help you with just base tasks, right, Like,
you know my granddad, one granddad went to a home
the other granddad had in home care, and the one
that had in home care. It was very simple things.
Remind you to take your medication and bring the medication over,
(33:25):
get you a glass of water, help you to get
up and out of bed, you know, to go to
the bathroom. Just help you stabilize to go to the bathroom.
And so that's not something that we're you know, we're
largely paying attention to industrial applications right now, but that
is the dream long term. So I'll be excited to
see how it shakes out.
Speaker 1 (33:46):
Yeah, rationally, what you were saying is it makes sense,
Like I understand most people would rather stay at home.
I understand that in home care is like impossibly expensive
for most people. At the same time, like my emotional
response to a robot taken care of, say, my parents,
is it makes me feel sad. And I recognize that
that's perhaps irrational, but that is at some level my
(34:08):
emotional response. But you know, the happy thing is like
I should take care of them, but like that's hard
and it's probably not gonna happen, and it's for its
own set of reasons. Right, it's more more than we
bargained for in this conversation. I don't know, No, there
is something though a humanoid robot starts to get to
some weird places in that way.
Speaker 2 (34:25):
Right, Yeah, I've thought a lot about this and and
I think it's a great place to go to happy
to talk about it. I think what we want is
we want humans taking care of other humans. That's what
we want, right, But we don't have that today where
you know, look at the way that we age. You know,
for me, you know, I was very close to both
of my granddads. They both lived into their nineties and
(34:46):
outlived my grandmother's oddly enough, and so I sort of
watched them age through their lens and that was a
big driver of doing this. And you know, these are
people that both of them were war heroes, they contributed society,
they did all these amazing things, and then at the
end of their life, they felt like they were a
(35:07):
burden to their family, and they had this feeling like
they never had to rely on anyone for anything, and
now they're completely reliant on people for everything. And what
I saw them do as they age was they lost
their dignity. And for me, this idea that you could
have a machine that carries your secrets, that is, your
(35:31):
machine that does things for you, allows you to keep
your dignity such that then you as a human that's aging,
you're fresher. You don't have to rely on your son
or your daughter or your spouse to get you a
glass of water or to do things for you. You
still have your own agency and your own autonomy through
(35:52):
a machine, and that then helps your family to be
much fresher because they don't have the burden of having
to do all these things to support you, where then
they can be fresher. And so my hope is that
this is not designed to replace what humans do for
each other. This is designed to augm and enhance that. Remember,
just like my granddad as he was getting older, you know,
(36:15):
I was working and I was busy, and you know,
I would try to go over as many days as
I could, but it was always really tough and I
didn't want to be alone, and it was this whole
battle that I think everybody goes through. And my hope
for the future, actually think it's a much more optimistic
version is that hopefully my parents have a robot, and
that robot is basically programmed for their happiness, and it's
(36:38):
designed to remind them when they're down of their favorite
song and play it right, remind them that of the
movies that they like watching, or whatever, it might be right.
And I think that's more optimistic. I think that's exciting,
and that makes me hopeful about the future. And you know,
I think that's the worst part of the human experience
is the way that we age. And I think that
(36:59):
these robots and AI embodied AI and AI in general
can hopefully allow us to take better care of each other.
So I don't think it is creepy. I think it
can actually be pretty beautiful if properly done. And that's
what you asked at the beginning. How is eptronic different
and what are we focused on? You know? For me,
(37:22):
I say human centered robotics. But what that means is
that we want this to be an optimistic future for humanity.
We are tool makers, and we want to build tools
for humans to enable us to live in better ways.
And I think that if we really focus on that,
I think that there can be really amazing ways of
(37:44):
doing this, and I think elder care is a great
example of how this can be used in that way.
Speaker 1 (37:53):
We'll be back in a minute with the Lightning Round.
And now we are back, as promised with the Lightning Round.
What's the biggest difference between Austin today and Austin ten
years ago?
Speaker 2 (38:14):
Oh man, it's changed quite a bit. So. One of
the things that made Austin, you know, really a great
place to live and work was just how small it
was and how accessible everybody was. You know, we used
to have these house parties and somebody would bring a violin,
and someone would bring a sitar and these world instruments,
(38:35):
and you'd have just all sorts of eclectic, creative people
doing really interesting things. And so I think, you know,
one of the things that I am worried about is
that was kind of what made Austin special and the
things that make you special. People want to kind of commercialize, right,
and they want to they want to take this and
(38:56):
they want to sort of scale it, and and it's
almost special because it's not commercialized. It's just this raw,
organic thing. And and so how does that as more
tech and more money comes into Austin, you know, how
does that? How does what make Austin, what made Austin great?
How does that continue to evolve? So I think though
(39:17):
I welcome it, you know, I'd rather be in the
place where everybody's coming and everyone wants to build the future.
So I'm not one of those that is sort of
resisting the changes. I think it's really exciting, and I
think more people with new ideas about the future in
the world and kind of a free place to do it.
There's a you know, there's a real ability here in
(39:38):
Texas and in Austin to kind of do what you want,
and there's a real culture around you know, the freedom
to do the things that you want to do. And
so it's a kind of a unique place where all
that's coming to the creativity and the you know, the
capitalism and all that's all coming together. Is Austin still weird?
(40:00):
Still there's pockets of weird. Yeah, certainly there's still weird.
Austin is still there. It's all growing up. But yeah, certainly.
Speaker 1 (40:10):
What's your favorite humanoid robot in fiction, in books and movies? C?
Speaker 2 (40:16):
Three po for sure.
Speaker 1 (40:18):
Okay, you were ready with that. You have that one
on deck.
Speaker 2 (40:21):
Yeah, well for three point it makes C three po
the human helper. Right.
Speaker 1 (40:25):
What's one thing that you've learned about the human body
from building robots?
Speaker 2 (40:31):
Oh? Man, At a high level, what I've learned is
how amazing the human body really is. I think there's
this fear from humans that as we sort of continue
down this pursuit of replicating humans and building machines that
can do what humans do, that that diminishes what it
means to be humans. But what it's actually done for
(40:54):
me and most of the people working on this is
it just makes you appreciate even more how amazing humans are.
So the hand is something that you think a lot about.
You just do all these things and you don't appreciate
how incredible your hands are are. And you just when
you when you start to try to build a hand
(41:15):
for a robot, you just appreciate all the limitations how
we walk, how we move, the fact.
Speaker 1 (41:22):
That we can go hard right, all the things we
do just like pick up an egg or open a
door like that's wildly difficult.
Speaker 2 (41:29):
It's amazing. Or you eat that egg and it powers
you for a day. It powers this neural network brain
that's you know, billions of parameters, right. I mean, the
human human humans are amazing. And I think as we
continue to learn more about what it means to be human,
what does it mean to be conscious, all these kind
(41:50):
of big ideas I think will only grow to appreciate
what we actually have here.
Speaker 1 (41:55):
Last one, tell me about your grandfather.
Speaker 2 (41:59):
Oh man, So you know two grandfathers, one Gilberto Cardinas,
the other one George Smith. Both of them were great.
My granddad, Gilberto Cardinas, came from Puerto Rico. When he
was seventeen, he joined the army and fought in the
(42:20):
Korean War. He spoke five languages. He was self educated,
and you know, he had the American dream and dreamed
of what he could do. He was in the army,
he was actually a field medic but became a hospital administrator,
and he's a big sort of driving force in our family.
(42:41):
And I watched him age and watched all the things
he went through. He actually fell and lost his vision,
so his brain was still intact in his body largely,
but he couldn't see, and so he had to have
around the clock care when he was in his nineties
in the home. And he wasn't wealthy by any stretch,
(43:01):
but he'd done okay and had saved his money, and
it was seventeen thousand dollars a month for in home care.
And it was like this revolving door of people that
would rather be doing anything else than sitting in a
room with my granddad and taking care of him. And so,
you know, for me, I admired my granddad so much,
(43:25):
and just seeing sort of that as the end of
his life, sitting in a room, you know, counting the
days down, I just thought there's got to be a
better way than this, And that was a big, a
big driver for me doing all this. My other granddad
actually ended up getting George Smith went to he had
to go to a home. He had colon cancer and
(43:47):
his brain still functioned. He had a great sense of
humor and he lost control of his bows. And so
you can imagine how humiliating that is as you age
to be fully aware of what's going on, never rely
on anybody, but not be able to control your bows.
And so he had to get multiple showers a day,
(44:08):
and every time I would go see him, you know,
just it was a humiliating experience. And so these are
things that are just my story, but everybody has their
own story of you know, taking care of an aging
parent or grandparent and just what that looks like. And
and my hope is that as humans, as tool makers,
(44:29):
I think we can do better than that. And I
think that these machines we can create will allow us
to take better care of each other. And my parents
are already naming their robots. They're there there, they can't
wait to get them. They're like, you know, they're they're
almost seventy now, and they're like, you got to have
these ready for whenever, whenever our time is there, so
(44:51):
that you know, so that we can age more gracefully
than our parents did.
Speaker 1 (45:02):
Jeff Gardinas is the co founder and CEO of Actronic.
Today's show was produced by Gabriel Hunter. It was edited
by Lydia Jeane Kott and engineered by Sarah Bruguer. You
can email us at problem at Pushkin dot Fm. I'm
Jacob Goldstein, and we'll be back next week with another
episode of What's Your Problem.
Pushkin As a general matter, I'm a fan of technological progress,
but I'll admit that humanoid robots kind of creep me
out for you know, obvious, normy, uncanny Valley type reasons.
And yet there is an exchange that you'll hear near
(00:36):
the end of today's show. That's the most compelling argument
I've ever heard for humanoid robots. And it's not just
an intellectual argument, it's an emotional argument. If that's a phrase,
it's really a very human argument for humanoid robots. I'm
(00:59):
Jacob Goldstein, and this is what's your problem to show
where I talk to people who are trying to make
technological progress. My guest today is Jeff Cardinas. He's the
k founder and CEO of Eptronic. Jeff's problem is this,
can you make a safe, reliable humanoid robot for less
than fifty thousand dollars. We started our conversation talking about
(01:21):
the DARPA Robotics Challenge. DARPA, of course, is the government
agency that helped to create the Internet and that gave
a big push to early self driving cars, among other things.
And Jeff says the agency's robotics challenge, which happened a
decade ago. Happened in twenty fifteen played a key role
in launching a bunch of the companies that are now
(01:42):
working on humanoid robots.
Speaker 2 (01:46):
The Darker Robotics Challenge was a challenge that was created
in the wake of the Fukushima disaster. Lukashima happened in
the you know, there was a meltdown in the nuclear
reactors and it was unsafe for people to go in,
and in essentially what happened was they needed a robot
to go in to sort of prevent a broader catastrophe.
(02:08):
And as they went out to the robotics community, the
idea was calling all roboticists, we need a robot to
go in and to help out here. And there was
no robots that could do all the different tasks that
were required to actually get to the melted down reactors.
So you had to go down steps, you had to
open doors, had to do a whole range of things.
(02:29):
And in the wake of that, basically what DARPA said was, certainly,
there's got to be the technology to enable us to
have much more versatile robots for natural disaster relief so
this never happens again. So out of that, Darker created
something called the Darker Robotics Challenge, and so there's a
variety of teams around the world that were put together
(02:51):
to build these general purpose robots. And the team that
we came out of was the Nasagen and the Space
Center team working on a robot called Valkyrie.
Speaker 1 (03:03):
So I want to go back to this moment when
the DARPA Challenge and there's this big final test, and like,
what we have from it is not some incredible breakthrough,
but like a blooper reel of robots, what are they doing?
Falling downstairs or driving cars into walls or.
Speaker 2 (03:21):
Something pretty much. I mean, the blooper reels make it
seem worse than it was, but but yeah, we had
we basically the realization was the technology's not there. It's
going to take time to continue to mature until it
can get to the point where it's actually commercially viable.
Speaker 1 (03:40):
And so it's interesting, it's super interesting right that this
moment is not the like beginning of some humanoid robot winter,
but rather the beginning of this humanoid robot industry. Like
how does that work? Like how do people whatever start
companiesn't get money out of this seemingly disappointing moment.
Speaker 2 (04:00):
Well, I think it actually was in winter, you know,
when we started in you know, the company was incorporated
in twenty fifteen and we started in twenty sixteen, and
for the most part, nobody wanted to talk about humanoids
and nobody was really paying attention to it. There was
a handful of folks that I sort of think of
as the true believers that were really working on this
(04:21):
problem and thought, you know, we don't care how long
this is going to take. We're just going to keep
working on this no matter what. But for the most part,
the entire robotics industry was very anti humanoids coming out
of the Darker Robotics Challenge, and in fact, there was
many people that were saying they'll never be viable, like
why would you ever use a humanoid robot. They're too complicated,
(04:42):
they're way too expensive, and you'll always use a simpler robot.
So actually, most of the people that we met when
we decided to start Uptronic were doubters, and we're saying,
humanoids will never make sense. We'll use these special purpose robots.
You know, maybe in fifty years humanoids will make sense,
but not for a long time.
Speaker 1 (05:02):
I mean special purpose robots is a pretty compelling case, right,
like whatever we all have, you know, dishwashing robots and
clothes cleaning robots in our houses, and like, you know,
wheels seem way easier than legs for lots of things.
And obviously there have been robot arms for what I
don't know, seventy years now or something like. So robots
(05:25):
in a way are all around us. Why would you
build a machine that looks like a dude when that's
like wildly hard.
Speaker 2 (05:30):
Right, Yeah, I mean I was naive coming out of
graduate school, and to me it seemed obvious. And the
way I used to think about it was you could
either have thousands of robots that do one thing, or
you could build one robot that could do thousands of
different things. And when I would talk about this with Nick,
my co founder, Nick would say, look, you can either,
(05:51):
you know, invest all this engineering in each of these
sort of narrow solutions, or yes, a humanoid robot, a
viable humanoid robot could take you years, it could take
you a decade, But once you invest all this time
in this single platform, then you can reap the benefits
of that across You can spread the research of that
(06:13):
across many different applications.
Speaker 1 (06:15):
I mean, is there not a middle case where there's
like some core kind of functionality that you develop that
works across many different types of robots. Is that a
less straw many version of the non humanoid robot kind
of argument?
Speaker 2 (06:29):
I think there could be. But I came into robotics
and basically just saw a lot of challenges with the
business models. So you build this special purpose robot, you
custom program the robot. In the industrial space, you can
spend six times the price of the robot on just
systems integration, and then the robot just does one thing.
(06:50):
So this idea that you could have a much more
versatile robot, to me, seemed obvious, like, if robotics is
going to scale, we have to have much more versatile
robots that we've had in the past. So if you
sort of think of that as the premise is we
need more versatile robots, then the question is, well, how
do you get there and what does versatility mean? And
that's where it led me to the humanoid making a
(07:12):
lot of sense, because if you have to modify the
environment for every new task that the robot can do,
you run into the same problem that we had in
sort of classical robotics. But if the robot can retrofit
into the environment such that you don't have to change
or modify the environment for every new task that the
robot can do, then it seemed to me that this
(07:33):
would maybe be the key unlock for robotics to actually
scale to the masses.
Speaker 1 (07:38):
The demand would be infinite. If you had a thing
that was the size and shape of a person with
arms and legs like, scale would be off the charts,
And presumably that's what drives costs down, and that's like
the good flywheel.
Speaker 2 (07:50):
Right, yeah, exactly.
Speaker 1 (07:52):
Okay, so you had this big idea about humanoid robots
and you started a company. But at the moment you
started the humanoid robot company, the prevailing sentiment was like,
deeply skeptical, what happened?
Speaker 2 (08:03):
What did you do? Well? A handful of us kept
working on it, so I didn't know any better. You know,
sometimes it's better that you don't know any better. I
thought humanoids were really cool, and I thought that it
just seemed it made sense to me that, you know,
how are we going to get to millions of robots
that are working and you know, with and around humans
(08:24):
and all these environments, and to me, this seemed like
the only way that that was going to happen. And
the way I looked at it was, even if we failed,
this was a worthy pursuit and I would be proud
that I tried to do it. And so the way
that we did it was we bootstrapped the company. There
was no investors that were willing to invest in humanoid
robots at the time that we got started, especially for hardware,
(08:48):
which we can talk about that as we move forward.
And so we bootstrapped the company and we basically got
paid to build robots for a lot of different folks.
And for the first five years of the company, we
just built the company on revenue. We would get project
after project and somehow never died.
Speaker 1 (09:08):
Like what kind of were you taking at that time?
What's an one example, Well, we.
Speaker 2 (09:12):
Had our first contract was with NASA, So we had
a contract with NASA to build Valkyrie two, to take
the lessons learned from the DARPA Challenge and build the
next iteration of Valkyrie. We were really kind of pioneering
new ways of building these systems. So US Special Forces
ended up coming to us about a year in and said, hey,
we want to do Ironman suits, and our view was
(09:33):
this was kind of a humanoid robot that you wear.
We worked in automotive. We helped build humanoid robots for
a couple you know, major companies that are still working
on these things today, and we would help sort of
pioneer new ways of building their platforms. So we've done
fifteen unique robots since we got started, and we're now
(09:54):
in a ninth iteration of humanoid and I have only
raised money in the last couple of years.
Speaker 1 (09:59):
So where'd the idea to build a robot for fifty
thousand dollars come from?
Speaker 2 (10:08):
The idea of where to build a robot for fifty
thousand dollars was what will it take for these robots
to be economic and reach mass market? So, you know,
when we got started, sort of my view was, you know,
what will a truly viable commercial humanoid look like and
what would the bomb costs need to be for this
to make sense? And if you sort of just do that,
(10:30):
bottoms up about fifty thousand dollars for the robot, you're
in the money for mass market. You can still do
some tasks in a very economic way at even one
hundred thousand or one hundred and fifty thousand, but fifty
thousand was the goal. This has now been blown by
by some of the new entrepreneurs that are coming out
that are talking about sub twenty thousand dollars. But it
(10:52):
never made sense to me that robots were as expensive
as they were at the time. If you look at
a humanoid compared to a car, there's about four percent
the raw material by weight. So one of our robots
there's about three hundred dollars of raw aluminum, which is
the the base metal of the system. And so it
(11:14):
never made sense to me that these robots would need
to be any more than fifty thousand dollars. As you
could reach scale and as you can start to think
about new ways of building them in similar ways that
we build other machines.
Speaker 1 (11:26):
So you decide you want to build a fifty thousand
dollars robot, Like, what do you actually do to do that? Like,
how do you go from having an idea of building
a fifty thousand dollars robot to having a fifty thousand
dollars robot.
Speaker 2 (11:40):
Well, you iterate until you solve any problem. So what
we would do is basically we would get a project
or a contract to build a robot, and we would
put a lot of different ideas into those designs. In
early days, it was all about performance. How can you
get the robot to just do these tasks, to stand
(12:01):
and have a battery life that's long enough. And then
as we kept evolving, we started to really focus on
cost in addition and scalability and assemblability and robustness. And
the key building block to drive cost and performance is
the actuator. So I mentioned we've done nine iterations of humanoids,
but we've done sixty iterations of electric actuators.
Speaker 1 (12:24):
Actuators are basically the thing that makes the robot.
Speaker 2 (12:27):
Move, right, Yeah, the muscles of a robot.
Speaker 1 (12:31):
So where are you now? Tell me about Apollo.
Speaker 2 (12:34):
Yeah, we're now at an exciting point. We have about
one hundred and seventy employees at Electronic. We are piloting
these robots right now. So I think the entire industry
is still in the pilot stage overall. There's some commercial
orders that are happening, but still early days for humanoids.
We're working with a handful of really great partners, folks
(12:55):
like Mercedes and GXO, and we're getting the robots out
into the real world and in pretty big ways, and
so we'll have more announcements over this year. We have
a big partnership with Google Deep Mind, which is something
that I always dreamed of coming out of graduate school.
We had a lot of respect for the folks that
at Google, and they have a whole history and legacy
(13:17):
in the humanoid space as well. And basically right now
we're getting these robots out into the world and gearing
up for you know, real commercialization, which we expect to
happen in twenty twenty six.
Speaker 1 (13:32):
What's the robot look like?
Speaker 2 (13:33):
The robot kind of looks like a superhero. Maybe this
has been kind of the idea that we've had from
the beginning. It's got two eyes and a face. It's
five foot eight, weighs one hundred and sixty pounds, has
four hour swappable batteries. Yeah, it's got a screen on
its chest and a face and that's about it.
Speaker 1 (13:53):
Two arms, two legs. What's to have in the way
of hands.
Speaker 2 (14:00):
So it has hands, has five fingered hands. You know,
there's these debates that I think of his false debates
in the humanoid space. So a lot of people when
they sort of knock humanoids and the viability of humanoids.
It usually has to do with do they need legs
and do they need hands? And the answer to that
question for me is no, they don't. It's a robot,
and robots are modular. So we can put Apollo on
(14:21):
any mobility base. We can put it on wheels, we
could put it on tracks, we could stationary amount the
upper torso of Apollo. And the same thing's true for
the hands of the grippers. We can use parallel grippers,
or we can use five fingered hands.
Speaker 1 (14:37):
Hands are like a whole thing, right, Like hands are?
Is it partly because they're so hard? Like what's going
on with robots and hands?
Speaker 2 (14:44):
Yeah, it turns out hands are a whole thing. This
is another one of those things that you know. It's
it's almost better that you don't understand the complexity before
you get into it, or else you might not have
done it in the first place. Ninety eight percent of
all tasks that humans do are done with our hands.
So there are narrow things that humanoids can do without
(15:06):
more dexterity, but it's very limited relative to the whole
sort of you know, all the different types of tasks
that humans do. Most of the things we do involve
our hands, and certainly in the industrial space, most of
the work is done with hands, So solving the end
defector or the hand problem is a big deal. There's
a lot of different debates about what you need and
(15:28):
how you get something that can actually perform industrial work.
You know, we've chosen the five finger hand route and
we're working across the space to really make some big
advancements there.
Speaker 1 (15:41):
Overall, it's part of the trade off, Like I could
build whatever two what do you call them prongs? Like
if you had two fingers, basically like a claw, Like
you could do a lot of things with a claw.
Presumably it would be way easier, but you couldn't do everything.
Is it kind of like what are you optimizing for?
And sort of how much payoff now versus how much
payoff later?
Speaker 2 (16:01):
Yeah, I think that's exactly right. It's you know, versatility,
you know, compared to robustness and cost. Basically, how much
complexity do you want to have on the system. And
you know, for these robots to be really viable in
the long run, especially in the industrial space, they've got
to be able to operate two shifts a day minimum.
(16:22):
Really you know, twenty two hours a day, seven days
a week. But solving that problem in a hand, so
just getting the performance of the hand first, but then
the robustness for them to do that type of work
is the next piece. And that's a trade off of
performance and complexity and cost.
Speaker 1 (16:39):
Because like it gets delicate, right, presumably the fingers so
to speak, would be fragile, right, Yeah, they can bring
easy to break.
Speaker 2 (16:47):
Yeah, yeah, yeah, and you've got to maintain it and
you've got to support those systems and fix them out
in the field. And so what's the trade off there?
And that's a whole trade space that we've been working
on over a long time.
Speaker 1 (17:00):
So we've been talking about hardware. Let's talk about the
software side. What's happening with that.
Speaker 2 (17:08):
A lot's happening on that side. I think we're really
in a really you know, we're an exciting point for
robotics overall. Think of the AI as really the last
piece of the puzzle. So, you know, we've had the
ability to build complex robots for a relatively long time.
We're just now, you know, really figuring out how to
(17:28):
take the lessons from automotive and consumer electronics and and
build much more economic systems, and we've had some advancements
and things like motors and batteries and compute and sensors
that have all sort of built up to this moment.
But the final piece of the puzzle was the AI
and the intelligence and essentially the way to think about it.
(17:48):
And I think Jensen does a great job of explaining this.
Speaker 1 (17:50):
But the advances Jensen Wong from Nvidio, Yeah, that's right.
I feel like Jensen is not quite the Elon level
of one name, household name, but sorry, go on, he's
getting there.
Speaker 2 (18:02):
Yeah, he should be.
Speaker 1 (18:04):
He should be. He should be.
Speaker 2 (18:07):
Basically, the advancements in geni AI turn out to apply
very well to robotics, and particularly to humanoid robotics, So
you can basically map human movement and trajectories from humans
doing things and build big data sets and use that
to train robots to do similar tasks in similar environments.
And these transformer architectures that we're using and generative AI
(18:31):
actually apply very well to robotics, and so this has
been a big sort of breakthrough moment for robotics. And
so I think as an industry as a whole, everybody's
really excited right now because we're reaching new heights and
we're able to do things that we dreamed about doing
with robots only you know, even a few years ago,
are now possible, and we're seeing a really rapid advancement
(18:53):
in performance overall.
Speaker 1 (18:54):
What's an example of a thing that you could only
dream of a few years ago that robots can do now?
Speaker 2 (19:00):
I think it's more dexterity and versatility. So just the
range of things that you can do. So the challenge
for robotics was that each news even if you build
something like a humanoid robot, and this is true for us,
and say you build an application to pick boxes off
of a palette and place those boxes onto a conveyor. Well,
(19:21):
you hand build that application, and you know, maybe takes
you eighteen months to sort of ring that out and
get it to a certain amount of robustness. Well, now
you want to do the reverse of that and pick
off of a conveyor and palletize something that will take
you the same amount of time that it took you
to build the initial application.
Speaker 1 (19:41):
You have to write basically have to write a whole
other piece of software. You have to start from scratch
almost yeah.
Speaker 2 (19:47):
Yeah, exactly. And so basically what is happening now is
that we now have these much more sort of general
models that where you can collect a lot of data
at the top layer, and so each new task that
you want to perform actually takes less and less incremental
amount of work. So what it's opening up now is
(20:08):
more dexterous applications.
Speaker 1 (20:13):
Still to come. On the show how Jeff's grandparents inspired
his work on robots, So you were talking about using
the transformer model. That has been the you know, breakthrough
that has driven large language models in training robots essentially.
(20:38):
I mean, of course, a key sort of serendipitous thing
that happened with language models was there is this crazy
large data set of words and pictures which is the Internet,
and there's not an analogous data set for the physical world. Right, Yeah,
it seems like that is is that the rate limiting step?
(20:58):
Is that the big problem in sort of AI for robots?
Speaker 2 (21:03):
Yeah, I mean there's a lot of work that's still
happening at the research level for you know, how can
you pull that kind of data from from videos so
you can think of big data sets of humans doing
things that could be really interesting to train robots in
the future. Interest and that that will come into play
over time. But yeah, it's the chicken or the egg problem.
(21:23):
And data is the one of the key things that
we need to enable the next wave of breakthroughs. And
and this is kind of the race, is can you
get robots out into the to the real world, into
the field and collecting data very high you know, in
very high volumes. Whoever does that, you know, will we'll
have better models. And this is the data flywheel. So
(21:46):
this is kind of the race that's on right now
where you hear a lot of other humanoid CEOs talking
about getting a lot of robots out into the world.
Largely those are going to be under Teley operation collecting
data and then you know, training and building these models
of the future.
Speaker 1 (22:01):
Ah So so it's like whoever gets there first will win,
just because that'll be the accelerant. Like once you have
robots in the world and you're collecting data, then you're
immediately getting ahead of whoever has fewer robots out in
the world because they're collecting less data.
Speaker 2 (22:16):
Yeah, so.
Speaker 1 (22:18):
Tell me about Telly operation.
Speaker 2 (22:21):
Tellyoperation is basically just remotely controlling the robot, so you're
taking over the robot. You can see through the robot's
eyes with a VR headset, and then you're controlling the
robots arms and hands to do a particular task. It's
like a video game and you're controlling a robot. It's
the simple idea. There's a couple of reasons it's important.
(22:44):
The first thing is that it tells you what the
robot's physically capable of doing. So if I'm completely controlling
the robot and I can't do a task under Telly operation,
then that means the robot's not physically capable of doing
it to be very difficult for an AI control system
to control the robot to do that. So this is
how we understand the physical capabilities of the robot as
(23:05):
these new models have come along. The simple idea is
that if you can tell you operate the robot to
do a task, then you should be able to automate
that task on the other end. So if you can
collect enough data under Telly operation, then you can automate
it by running it through these similar architectures that we
talked about.
Speaker 1 (23:25):
And so's the basic idea that like you use remote
control to like drive the robot to do a thing
whatever A thousand times some number of times, and in
doing that, you're training the robot, you're training the software
training the I.
Speaker 2 (23:40):
Yeah, that's exactly right.
Speaker 1 (23:42):
What's an example of a thing that you've done that way?
And like, you know, how many times did you have
to remote control it before the robot could do it?
Speaker 2 (23:48):
So each is picking is a good example. Or you
know you're taking objects and you're putting them into a
box to do that in a simple context, thousands of
demonstrations is what you need, you know, and we think
of this as generally ours. So you know, how many
hours of data collection have we done, and thousands of
(24:11):
iterations can get you to let's say, eighty percent of
human rate. If you want to get to ninety five
percent or better of human rate, then you need more
and more data. But it's in the thousands, it's not millions.
Speaker 1 (24:24):
Yeah, thousands makes it seem totally tractable.
Speaker 2 (24:27):
Yeah, that was actually surprised by how well these models
work and actually how little data they need to get
relatively good performance. And you're seeing a lot of demonstrations
of this out there today.
Speaker 1 (24:40):
And presumably that'll get better and better. Right as the
software side of AI gets better and better, it'll learn
faster essentially and the other obvious thing, but I'm just
going to say it is like, once you have done
it once, then it works for every robot. Then you
can make a million robots and they all know how
to do the thing, right.
Speaker 2 (24:57):
Yeah, that's exactly right. And one of the interesting things
about these models is actually the diversity of data is
almost more important than task specific data. So you want
to go wide across a range of tasks, and then
you're basically building all these skills into the robot, and
then it becomes better at doing any one particular task.
Speaker 1 (25:17):
It really is like learning.
Speaker 2 (25:19):
It really is a human esque Yeah, that's right.
Speaker 1 (25:22):
So I know you're in a few pilot projects with
Mercedes and what is GXO big logistics company. When do
you want to start selling robots for real? Like when
do you think that might happen?
Speaker 2 (25:33):
Twenty twenty six?
Speaker 1 (25:35):
Okay, yeah, suddenly that's next year. Almost.
Speaker 2 (25:39):
Now, a year is a long time in you know,
these are dog years. It's a long time in this space.
Speaker 1 (25:48):
And twenty twenty six could be almost two years.
Speaker 2 (25:50):
Yeah.
Speaker 1 (25:51):
Now, like who are you going to sell robots to and
how much you're going to charge and what are they
going to.
Speaker 2 (25:57):
Do so the initially in manufacturing and logistics, so folks
like Mercedes and GXO, these are the initial customers of
these systems. We are not announcing pricing yet, but you
can think of it as you know, take what you
know it costs to do these tasks today and with
(26:17):
some discount to what it costs to do these tasks today.
We have a rass model that we that we use,
so you basically robot as a service. Yeah, robot as
a service model where you're you're paying the robot basically,
you know, by the hour effectively to do a particular task,
and that's at a discount to what it costs to
(26:38):
do that task today.
Speaker 1 (26:40):
How far are you from the fifty thousand dollars robot.
Speaker 2 (26:43):
We're not there yet, so not very far. So we
have the architecture to be able to do this. So
getting the cost down on these robots is a two
step process. So first step is new architectures. So if
you still require this very high precision in the system
and you're using bespoke components that are only used for robotics,
(27:05):
these robots will still be expensive. The challenge of umanoid
robots is they have a lot more motors than traditional robots.
So traditional robot has six or seven motors, a humanoid
robot has thirty to forty plus.
Speaker 1 (27:19):
Okay, so that means it's expensive where you've got to
figure out how to get cheaper actuator.
Speaker 2 (27:25):
Yeah, so we're there. So for us, that was a
five hundred dollars actuator that we and we have a
five hundred dollars actuator now today. And so once you
solve that problem, and once you solve the architecture problem,
now it's about scale and manufacturing. So a lot of
where we spend, a lot of where the cost is
driven at low volumes is in just the structures of
(27:45):
the robot or we're seeing seeing we're milling at of
big blocks of metal parts and very small quantities. But
there's other techniques that are are much more cost effective,
like casting or stamping, and these will allow these robots
to be much cheaper. As I mentioned, look at automotive
(28:06):
and look at the scale of automotive, there's four percent
the raw material by weight and a humanoid robot as
compared to a car. So as you are once you
solve the architecture problems such that you can build a
lot of these systems, and you are, they're simpler to
make than The next piece is just applying mass manufacturing
approaches to this to make them a lot cheaper. As
(28:29):
you skin, well, I mean.
Speaker 1 (28:33):
That's a hard leap to make, right, Like what do
you do? You get a ton of capital and just
build a factory and hope there's demand on the other end, Like,
how do you go from this bespoke expensive thing to
a mass produced, you know, much less expensive thing.
Speaker 2 (28:48):
Well, it's a gradient. So, like I said, step one
is you have new approaches that allow you to make
them cheaper, just inherently on a unit to unit basis.
So the early humanoids were like millions of dollars and
now we're in the hundreds of thousands of dollars range
for building one.
Speaker 1 (29:04):
So you just got to get one more order of
magnitude out of it.
Speaker 2 (29:08):
Yes, they've already We've drop the price by an order
of magnitude. And then now as we build more, and
even as you go as you add a zero, as
you go from ten to one hundred, the price drops
pretty dramatically. So you don't need the volume that you
that you might think, like my view is we can
get to the sub fifty thousand dollars price point in
the thousands of unit quantity, so without without hundreds of
(29:31):
thousands or millions of.
Speaker 1 (29:32):
These So one big buyer, one one big car company
or logistics company might place an order of thousands of units.
Speaker 2 (29:39):
Right, Yeah, And you made a comment that you said,
and you hope that there's demand. One of the things
that I think is important to note is the demand
for these robots is enormous. We have demand for hundreds
of thousands of units already today with the customers that
we're working with. So the demand is enormous. So we're
(30:01):
we're ramping up. You know, we've got to get the
robustness and the safety of the system and really bring
out the design. And you know, we're these are you know,
really credible, thoughtful people that are coming from other industries
that are now joining us, that now see that we've
crossed this threshold of technical viability and now taking lessons
(30:23):
from you know, how you scale and manufacture other things
and bringing that into the robotic space and the humanoid
space overall.
Speaker 1 (30:31):
So in a year or at least next year, you
want to be selling robots for real? Where do you
want to be in five, say years.
Speaker 2 (30:41):
My view is that where this evolves is it's going
to start in logistics and manufacturing, and then as we
solve safety as an industry. I'm really interested in healthcare
and particularly in elder care over time, so you know,
how can these robots be used to improve the way
we live and work. That was really the lens that
I came into this on, and so I think over
(31:04):
the next five years you'll start to see the early
stages of the next three years just to see early
applications for robots entering the home. There's some folks that
are really working hard on this. I think we're about
three years out from that being really viable, but I
hope people prove me wrong. I hope it's faster than that.
Speaker 1 (31:24):
Three years seems fast. What's the sort of first first
use case, first job you imagine a robot doing for
real in somebody's house in three years.
Speaker 2 (31:35):
Well, everybody wants laundry. If everybody I talked to says,
when is this thing going to do my laundry? And
I want that as well.
Speaker 1 (31:42):
There's literally already a machine to do your laundry. All
you have to do is put it in one machine
and then put it in another The remaining work is trivial.
Speaker 2 (31:50):
Yeah, I mean, look, I'm not the person to talk
about the home. I think we're still a ways out.
But there's humanoid companies like one X that are really
focused on on the home, and we've got a lot
of respect for what they're doing over there, and so
I hope they do it. I know that they're working
hard on it. And you know where I want a
(32:12):
robot for the home as well, So you a lot
of the things that are that are happening. And with
these models that I talked about, these more generic models,
the things that we're learning in the industrial base can
apply to the home over time as well.
Speaker 1 (32:25):
In terms of the AI models, sure, I mean the
AA models are basically teaching a robot how to how
to deal with the physical world, that's right, how to
move around, how to pick things up, how to put
things down.
Speaker 2 (32:35):
Yeah, the task and the I mean the home's tough
because like, how much is it even a robot does
your your dishes, your laundry, cleans and cooks for you.
How much are you willing to pay for that on
a yearly basis?
Speaker 1 (32:47):
I'm imagining the first household tasks. I would have thought
you would have said, like people who are quadriplegic, right, Like,
there are a lot of people who have various kinds
of mobility problems who can't do very basic things around
the house, where essentially a robot could do it for them,
Like I would think that would be the first use case.
Speaker 2 (33:03):
I think that's a great use case. And you know,
for me, that's that's sort of in the realm of
what I say is older care, which is like assistive
robots that help you with just base tasks, right, Like,
you know my granddad, one granddad went to a home
the other granddad had in home care, and the one
that had in home care. It was very simple things.
Remind you to take your medication and bring the medication over,
(33:25):
get you a glass of water, help you to get
up and out of bed, you know, to go to
the bathroom. Just help you stabilize to go to the bathroom.
And so that's not something that we're you know, we're
largely paying attention to industrial applications right now, but that
is the dream long term. So I'll be excited to
see how it shakes out.
Speaker 1 (33:46):
Yeah, rationally, what you were saying is it makes sense,
Like I understand most people would rather stay at home.
I understand that in home care is like impossibly expensive
for most people. At the same time, like my emotional
response to a robot taken care of, say, my parents,
is it makes me feel sad. And I recognize that
that's perhaps irrational, but that is at some level my
(34:08):
emotional response. But you know, the happy thing is like
I should take care of them, but like that's hard
and it's probably not gonna happen, and it's for its
own set of reasons. Right, it's more more than we
bargained for in this conversation. I don't know, No, there
is something though a humanoid robot starts to get to
some weird places in that way.
Speaker 2 (34:25):
Right, Yeah, I've thought a lot about this and and
I think it's a great place to go to happy
to talk about it. I think what we want is
we want humans taking care of other humans. That's what
we want, right, But we don't have that today where
you know, look at the way that we age. You know,
for me, you know, I was very close to both
of my granddads. They both lived into their nineties and
(34:46):
outlived my grandmother's oddly enough, and so I sort of
watched them age through their lens and that was a
big driver of doing this. And you know, these are
people that both of them were war heroes, they contributed society,
they did all these amazing things, and then at the
end of their life, they felt like they were a
(35:07):
burden to their family, and they had this feeling like
they never had to rely on anyone for anything, and
now they're completely reliant on people for everything. And what
I saw them do as they age was they lost
their dignity. And for me, this idea that you could
have a machine that carries your secrets, that is, your
(35:31):
machine that does things for you, allows you to keep
your dignity such that then you as a human that's aging,
you're fresher. You don't have to rely on your son
or your daughter or your spouse to get you a
glass of water or to do things for you. You
still have your own agency and your own autonomy through
(35:52):
a machine, and that then helps your family to be
much fresher because they don't have the burden of having
to do all these things to support you, where then
they can be fresher. And so my hope is that
this is not designed to replace what humans do for
each other. This is designed to augm and enhance that. Remember,
just like my granddad as he was getting older, you know,
(36:15):
I was working and I was busy, and you know,
I would try to go over as many days as
I could, but it was always really tough and I
didn't want to be alone, and it was this whole
battle that I think everybody goes through. And my hope
for the future, actually think it's a much more optimistic
version is that hopefully my parents have a robot, and
that robot is basically programmed for their happiness, and it's
(36:38):
designed to remind them when they're down of their favorite
song and play it right, remind them that of the
movies that they like watching, or whatever, it might be right.
And I think that's more optimistic. I think that's exciting,
and that makes me hopeful about the future. And you know,
I think that's the worst part of the human experience
is the way that we age. And I think that
(36:59):
these robots and AI embodied AI and AI in general
can hopefully allow us to take better care of each other.
So I don't think it is creepy. I think it
can actually be pretty beautiful if properly done. And that's
what you asked at the beginning. How is eptronic different
and what are we focused on? You know? For me,
(37:22):
I say human centered robotics. But what that means is
that we want this to be an optimistic future for humanity.
We are tool makers, and we want to build tools
for humans to enable us to live in better ways.
And I think that if we really focus on that,
I think that there can be really amazing ways of
(37:44):
doing this, and I think elder care is a great
example of how this can be used in that way.
Speaker 1 (37:53):
We'll be back in a minute with the Lightning Round.
And now we are back, as promised with the Lightning Round.
What's the biggest difference between Austin today and Austin ten
years ago?
Speaker 2 (38:14):
Oh man, it's changed quite a bit. So. One of
the things that made Austin, you know, really a great
place to live and work was just how small it
was and how accessible everybody was. You know, we used
to have these house parties and somebody would bring a violin,
and someone would bring a sitar and these world instruments,
(38:35):
and you'd have just all sorts of eclectic, creative people
doing really interesting things. And so I think, you know,
one of the things that I am worried about is
that was kind of what made Austin special and the
things that make you special. People want to kind of commercialize, right,
and they want to they want to take this and
(38:56):
they want to sort of scale it, and and it's
almost special because it's not commercialized. It's just this raw,
organic thing. And and so how does that as more
tech and more money comes into Austin, you know, how
does that? How does what make Austin, what made Austin great?
How does that continue to evolve? So I think though
(39:17):
I welcome it, you know, I'd rather be in the
place where everybody's coming and everyone wants to build the future.
So I'm not one of those that is sort of
resisting the changes. I think it's really exciting, and I
think more people with new ideas about the future in
the world and kind of a free place to do it.
There's a you know, there's a real ability here in
(39:38):
Texas and in Austin to kind of do what you want,
and there's a real culture around you know, the freedom
to do the things that you want to do. And
so it's a kind of a unique place where all
that's coming to the creativity and the you know, the
capitalism and all that's all coming together. Is Austin still weird?
(40:00):
Still there's pockets of weird. Yeah, certainly there's still weird.
Austin is still there. It's all growing up. But yeah, certainly.
Speaker 1 (40:10):
What's your favorite humanoid robot in fiction, in books and movies? C?
Speaker 2 (40:16):
Three po for sure.
Speaker 1 (40:18):
Okay, you were ready with that. You have that one
on deck.
Speaker 2 (40:21):
Yeah, well for three point it makes C three po
the human helper. Right.
Speaker 1 (40:25):
What's one thing that you've learned about the human body
from building robots?
Speaker 2 (40:31):
Oh? Man, At a high level, what I've learned is
how amazing the human body really is. I think there's
this fear from humans that as we sort of continue
down this pursuit of replicating humans and building machines that
can do what humans do, that that diminishes what it
means to be humans. But what it's actually done for
(40:54):
me and most of the people working on this is
it just makes you appreciate even more how amazing humans are.
So the hand is something that you think a lot about.
You just do all these things and you don't appreciate
how incredible your hands are are. And you just when
you when you start to try to build a hand
(41:15):
for a robot, you just appreciate all the limitations how
we walk, how we move, the fact.
Speaker 1 (41:22):
That we can go hard right, all the things we
do just like pick up an egg or open a
door like that's wildly difficult.
Speaker 2 (41:29):
It's amazing. Or you eat that egg and it powers
you for a day. It powers this neural network brain
that's you know, billions of parameters, right. I mean, the
human human humans are amazing. And I think as we
continue to learn more about what it means to be human,
what does it mean to be conscious, all these kind
(41:50):
of big ideas I think will only grow to appreciate
what we actually have here.
Speaker 1 (41:55):
Last one, tell me about your grandfather.
Speaker 2 (41:59):
Oh man, So you know two grandfathers, one Gilberto Cardinas,
the other one George Smith. Both of them were great.
My granddad, Gilberto Cardinas, came from Puerto Rico. When he
was seventeen, he joined the army and fought in the
(42:20):
Korean War. He spoke five languages. He was self educated,
and you know, he had the American dream and dreamed
of what he could do. He was in the army,
he was actually a field medic but became a hospital administrator,
and he's a big sort of driving force in our family.
(42:41):
And I watched him age and watched all the things
he went through. He actually fell and lost his vision,
so his brain was still intact in his body largely,
but he couldn't see, and so he had to have
around the clock care when he was in his nineties
in the home. And he wasn't wealthy by any stretch,
(43:01):
but he'd done okay and had saved his money, and
it was seventeen thousand dollars a month for in home care.
And it was like this revolving door of people that
would rather be doing anything else than sitting in a
room with my granddad and taking care of him. And so,
you know, for me, I admired my granddad so much,
(43:25):
and just seeing sort of that as the end of
his life, sitting in a room, you know, counting the
days down, I just thought there's got to be a
better way than this, And that was a big, a
big driver for me doing all this. My other granddad
actually ended up getting George Smith went to he had
to go to a home. He had colon cancer and
(43:47):
his brain still functioned. He had a great sense of
humor and he lost control of his bows. And so
you can imagine how humiliating that is as you age
to be fully aware of what's going on, never rely
on anybody, but not be able to control your bows.
And so he had to get multiple showers a day,
(44:08):
and every time I would go see him, you know,
just it was a humiliating experience. And so these are
things that are just my story, but everybody has their
own story of you know, taking care of an aging
parent or grandparent and just what that looks like. And
and my hope is that as humans, as tool makers,
(44:29):
I think we can do better than that. And I
think that these machines we can create will allow us
to take better care of each other. And my parents
are already naming their robots. They're there there, they can't
wait to get them. They're like, you know, they're they're
almost seventy now, and they're like, you got to have
these ready for whenever, whenever our time is there, so
(44:51):
that you know, so that we can age more gracefully
than our parents did.
Speaker 1 (45:02):
Jeff Gardinas is the co founder and CEO of Actronic.
Today's show was produced by Gabriel Hunter. It was edited
by Lydia Jeane Kott and engineered by Sarah Bruguer. You
can email us at problem at Pushkin dot Fm. I'm
Jacob Goldstein, and we'll be back next week with another
episode of What's Your Problem.
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