The following is a conversation with Peter Abbeel.
He’s a professor at UC Berkeley
and the director of the Berkeley Robotics Learning Lab.
He’s one of the top researchers in the world
working on how we make robots understand
and interact with the world around them,
especially using imitation and deep reinforcement learning.
This conversation is part of the MIT course
on Artificial General Intelligence
and the Artificial Intelligence podcast.
If you enjoy it, please subscribe on YouTube,
iTunes, or your podcast provider of choice,
or simply connect with me on Twitter at Lex Friedman,
spelled F R I D.
And now, here’s my conversation with Peter Abbeel.
You’ve mentioned that if there was one person
you could meet, it would be Roger Federer.
So let me ask, when do you think we’ll have a robot
that fully autonomously can beat Roger Federer at tennis?
Roger Federer level player at tennis?
Well, first, if you can make it happen for me to meet Roger,
let me know.
In terms of getting a robot to beat him at tennis,
it’s kind of an interesting question
because for a lot of the challenges we think about in AI,
the software is really the missing piece,
but for something like this,
the hardware is nowhere near either.
To really have a robot that can physically run around,
the Boston Dynamics robots are starting to get there,
but still not really human level ability to run around
and then swing a racket.
So you think that’s a hardware problem?
I don’t think it’s a hardware problem only.
I think it’s a hardware and a software problem.
I think it’s both.
And I think they’ll have independent progress.
So I’d say the hardware maybe in 10, 15 years.
On clay, not grass.
I mean, grass is probably harder.
With the sliding?
With the clay, I’m not sure what’s harder, grass or clay.
The clay involves sliding,
which might be harder to master actually, yeah.
But you’re not limited to a bipedal.
I mean, I’m sure there’s no…
Well, if we can build a machine,
it’s a whole different question, of course.
If you can say, okay, this robot can be on wheels,
it can move around on wheels and can be designed differently,
then I think that can be done sooner probably
than a full humanoid type of setup.
What do you think of swing a racket?
So you’ve worked at basic manipulation.
How hard do you think is the task of swinging a racket
would be able to hit a nice backhand or a forehand?
Let’s say we just set up stationary,
a nice robot arm, let’s say, a standard industrial arm,
and it can watch the ball come and then swing the racket.
It’s a good question.
I’m not sure it would be super hard to do.
I mean, I’m sure it would require a lot,
if we do it with reinforcement learning,
it would require a lot of trial and error.
It’s not gonna swing it right the first time around,
but yeah, I don’t see why I couldn’t
swing it the right way.
I think it’s learnable.
I think if you set up a ball machine,
let’s say on one side,
and then a robot with a tennis racket on the other side,
I think it’s learnable
and maybe a little bit of pre training and simulation.
Yeah, I think that’s feasible.
I think the swing the racket is feasible.
It’d be very interesting to see how much precision
it can get.
Cause I mean, that’s where, I mean,
some of the human players can hit it on the lines,
which is very high precision.
With spin, the spin is an interesting,
whether RL can learn to put a spin on the ball.
Well, you got me interested.
Maybe someday we’ll set this up.
Sure, you got me intrigued.
Your answer is basically, okay,
for this problem, it sounds fascinating,
but for the general problem of a tennis player,
we might be a little bit farther away.
What’s the most impressive thing you’ve seen a robot do
in the physical world?
So physically for me,
it’s the Boston Dynamics videos.
Always just bring home and just super impressed.
Recently, the robot running up the stairs,
doing the parkour type thing.
I mean, yes, we don’t know what’s underneath.
They don’t really write a lot of detail,
but even if it’s hard coded underneath,
which it might or might not be just the physical abilities
of doing that parkour, that’s a very impressive.
So have you met Spot Mini
or any of those robots in person?
Met Spot Mini last year in April at the Mars event
that Jeff Bezos organizes.
They brought it out there
and it was nicely following around Jeff.
When Jeff left the room, they had it follow him along,
which is pretty impressive.
So I think there’s some confidence to know
that there’s no learning going on in those robots.
The psychology of it, so while knowing that,
while knowing there’s not,
if there’s any learning going on, it’s very limited.
I met Spot Mini earlier this year
and knowing everything that’s going on,
having one on one interaction,
so I got to spend some time alone and there’s immediately
a deep connection on the psychological level.
Even though you know the fundamentals, how it works,
there’s something magical.
So do you think about the psychology of interacting
with robots in the physical world?
Even you just showed me the PR2, the robot,
and there was a little bit something like a face,
had a little bit something like a face.
There’s something that immediately draws you to it.
Do you think about that aspect of the robotics problem?
Well, it’s very hard with Brad here.
We’ll give him a name, Berkeley Robot
for the Elimination of Tedious Tasks.
It’s very hard to not think of the robot as a person
and it seems like everybody calls him a he
for whatever reason, but that also makes it more a person
than if it was a it, and it seems pretty natural
to think of it that way.
This past weekend really struck me.
I’ve seen Pepper many times on videos,
but then I was at an event organized by,
this was by Fidelity, and they had scripted Pepper
to help moderate some sessions,
and they had scripted Pepper
to have the personality of a child a little bit,
and it was very hard to not think of it
as its own person in some sense
because it would just jump in the conversation,
making it very interactive.
Moderate would be saying, Pepper would just jump in,
hold on, how about me?
Can I participate in this too?
And you’re just like, okay, this is like a person,
and that was 100% scripted, and even then it was hard
not to have that sense of somehow there is something there.
So as we have robots interact in this physical world,
is that a signal that could be used
in reinforcement learning?
You’ve worked a little bit in this direction,
but do you think that psychology can be somehow pulled in?
Yes, that’s a question I would say
a lot of people ask, and I think part of why they ask it
is they’re thinking about how unique
are we really still as people?
Like after they see some results,
they see a computer play Go, they see a computer do this,
that, they’re like, okay, but can it really have emotion?
Can it really interact with us in that way?
And then once you’re around robots,
you already start feeling it,
and I think that kind of maybe mythologically,
the way that I think of it is
if you run something like reinforcement learning,
it’s about optimizing some objective,
and there’s no reason that the objective
couldn’t be tied into how much does a person like
interacting with this system,
and why could not the reinforcement learning system
optimize for the robot being fun to be around?
And why wouldn’t it then naturally become
more and more interactive and more and more
maybe like a person or like a pet?
I don’t know what it would exactly be,
but more and more have those features
and acquire them automatically.
As long as you can formalize an objective
of what it means to like something,
what, how you exhibit, what’s the ground truth?
How do you get the reward from human?
Because you have to somehow collect
that information within you, human.
But you’re saying if you can formulate as an objective,
it can be learned.
There’s no reason it couldn’t emerge through learning,
and maybe one way to formulate as an objective,
you wouldn’t have to necessarily score it explicitly,
so standard rewards are numbers,
and numbers are hard to come by.
This is a 1.5 or a 1.7 on some scale.
It’s very hard to do for a person,
but much easier is for a person to say,
okay, what you did the last five minutes
was much nicer than what you did the previous five minutes,
and that now gives a comparison.
And in fact, there have been some results on that.
For example, Paul Christiano and collaborators at OpenAI
had the Hopper, Mojoko Hopper, a one legged robot,
going through backflips purely from feedback.
I like this better than that.
That’s kind of equally good,
and after a bunch of interactions,
it figured out what it was the person was asking for,
namely a backflip.
And so I think the same thing.
Oh, it wasn’t trying to do a backflip.
It was just getting a comparison score
from the person based on?
Person having in mind, in their own mind,
I wanted to do a backflip,
but the robot didn’t know what it was supposed to be doing.
It just knew that sometimes the person said,
this is better, this is worse,
and then the robot figured out
what the person was actually after was a backflip.
And I’d imagine the same would be true
for things like more interactive robots,
that the robot would figure out over time,
oh, this kind of thing apparently is appreciated more
than this other kind of thing.
So when I first picked up Sutton’s,
Richard Sutton’s reinforcement learning book,
before sort of this deep learning,
before the reemergence of neural networks
as a powerful mechanism for machine learning,
RL seemed to me like magic.
It was beautiful.
So that seemed like what intelligence is,
RL reinforcement learning.
So how do you think we can possibly learn anything
about the world when the reward for the actions
is delayed, is so sparse?
Like where is, why do you think RL works?
Why do you think you can learn anything
under such sparse rewards,
whether it’s regular reinforcement learning
or deep reinforcement learning?
What’s your intuition?
The counterpart of that is why is RL,
why does it need so many samples,
so many experiences to learn from?
Because really what’s happening is
when you have a sparse reward,
you do something maybe for like, I don’t know,
you take 100 actions and then you get a reward.
And maybe you get like a score of three.
And I’m like okay, three, not sure what that means.
You go again and now you get two.
And now you know that that sequence of 100 actions
that you did the second time around
somehow was worse than the sequence of 100 actions
you did the first time around.
But that’s tough to now know which one of those
were better or worse.
Some might have been good and bad in either one.
And so that’s why it needs so many experiences.
But once you have enough experiences,
effectively RL is teasing that apart.
It’s trying to say okay, what is consistently there
when you get a higher reward
and what’s consistently there when you get a lower reward?
And then kind of the magic of sometimes
the policy gradient update is to say
now let’s update the neural network
to make the actions that were kind of present
when things are good more likely
and make the actions that are present
when things are not as good less likely.
So that is the counterpoint,
but it seems like you would need to run it
a lot more than you do.
Even though right now people could say
that RL is very inefficient,
but it seems to be way more efficient
than one would imagine on paper.
That the simple updates to the policy,
the policy gradient, that somehow you can learn,
exactly you just said, what are the common actions
that seem to produce some good results?
That that somehow can learn anything.
It seems counterintuitive at least.
Is there some intuition behind it?
Yeah, so I think there’s a few ways to think about this.
The way I tend to think about it mostly originally,
so when we started working on deep reinforcement learning
here at Berkeley, which was maybe 2011, 12, 13,
around that time, John Schulman was a PhD student
initially kind of driving it forward here.
And the way we thought about it at the time was
if you think about rectified linear units
or kind of rectifier type neural networks,
what do you get?
You get something that’s piecewise linear feedback control.
And if you look at the literature,
linear feedback control is extremely successful,
can solve many, many problems surprisingly well.
I remember, for example, when we did helicopter flight,
if you’re in a stationary flight regime,
not a non stationary, but a stationary flight regime
like hover, you can use linear feedback control
to stabilize a helicopter, very complex dynamical system,
but the controller is relatively simple.
And so I think that’s a big part of it is that
if you do feedback control, even though the system
you control can be very, very complex,
often relatively simple control architectures
can already do a lot.
But then also just linear is not good enough.
And so one way you can think of these neural networks
is that sometimes they tile the space,
which people were already trying to do more by hand
or with finite state machines,
say this linear controller here,
this linear controller here.
Neural network learns to tile the space
and say linear controller here,
another linear controller here,
but it’s more subtle than that.
And so it’s benefiting from this linear control aspect,
it’s benefiting from the tiling,
but it’s somehow tiling it one dimension at a time.
Because if let’s say you have a two layer network,
if in that hidden layer, you make a transition
from active to inactive or the other way around,
that is essentially one axis, but not axis aligned,
but one direction that you change.
And so you have this kind of very gradual tiling
of the space where you have a lot of sharing
between the linear controllers that tile the space.
And that was always my intuition as to why
to expect that this might work pretty well.
It’s essentially leveraging the fact
that linear feedback control is so good,
but of course not enough.
And this is a gradual tiling of the space
with linear feedback controls
that share a lot of expertise across them.
So that’s really nice intuition,
but do you think that scales to the more
and more general problems of when you start going up
the number of dimensions when you start
going down in terms of how often
you get a clean reward signal?
Does that intuition carry forward to those crazier,
weirder worlds that we think of as the real world?
So I think where things get really tricky
in the real world compared to the things
we’ve looked at so far with great success
in reinforcement learning is the time scales,
which takes us to an extreme.
So when you think about the real world,
I mean, I don’t know, maybe some student
decided to do a PhD here, right?
Okay, that’s a decision.
That’s a very high level decision.
But if you think about their lives,
I mean, any person’s life,
it’s a sequence of muscle fiber contractions
and relaxations, and that’s how you interact with the world.
And that’s a very high frequency control thing,
but it’s ultimately what you do
and how you affect the world,
until I guess we have brain readings
and you can maybe do it slightly differently.
But typically that’s how you affect the world.
And the decision of doing a PhD is so abstract
relative to what you’re actually doing in the world.
And I think that’s where credit assignment
becomes just completely beyond
what any current RL algorithm can do.
And we need hierarchical reasoning
at a level that is just not available at all yet.
Where do you think we can pick up hierarchical reasoning?
By which mechanisms?
Yeah, so maybe let me highlight
what I think the limitations are
of what already was done 20, 30 years ago.
In fact, you’ll find reasoning systems
that reason over relatively long horizons,
but the problem is that they were not grounded
in the real world.
So people would have to hand design
some kind of logical, dynamical descriptions of the world
and that didn’t tie into perception.
And so it didn’t tie into real objects and so forth.
And so that was a big gap.
Now with deep learning, we start having the ability
to really see with sensors, process that
and understand what’s in the world.
And so it’s a good time to try
to bring these things together.
I see a few ways of getting there.
One way to get there would be to say
deep learning can get bolted on somehow
to some of these more traditional approaches.
Now bolted on would probably mean
you need to do some kind of end to end training
where you say my deep learning processing
somehow leads to a representation
that in term uses some kind of traditional
underlying dynamical systems that can be used for planning.
And that’s, for example, the direction Aviv Tamar
and Thanard Kuretach here have been pushing
with causal info again and of course other people too.
That’s one way.
Can we somehow force it into the form factor
that is amenable to reasoning?
Another direction we’ve been thinking about
for a long time and didn’t make any progress on
was more information theoretic approaches.
So the idea there was that what it means
to take high level action is to take
and choose a latent variable now
that tells you a lot about what’s gonna be the case
in the future.
Because that’s what it means to take a high level action.
I say okay, I decide I’m gonna navigate
to the gas station because I need to get gas for my car.
Well, that’ll now take five minutes to get there.
But the fact that I get there,
I could already tell that from the high level action
I took much earlier.
That we had a very hard time getting success with.
Not saying it’s a dead end necessarily,
but we had a lot of trouble getting that to work.
And then we started revisiting the notion
of what are we really trying to achieve?
What we’re trying to achieve is not necessarily hierarchy
per se, but you could think about
what does hierarchy give us?
What we hope it would give us is better credit assignment.
What is better credit assignment?
It’s giving us, it gives us faster learning, right?
And so faster learning is ultimately maybe what we’re after.
And so that’s where we ended up with the RL squared paper
on learning to reinforcement learn,
which at a time Rocky Dwan led.
And that’s exactly the meta learning approach
where you say, okay, we don’t know how to design hierarchy.
We know what we want to get from it.
Let’s just enter and optimize for what we want to get
from it and see if it might emerge.
And we saw things emerge.
The maze navigation had consistent motion down hallways,
which is what you want.
A hierarchical control should say,
I want to go down this hallway.
And then when there is an option to take a turn,
I can decide whether to take a turn or not and repeat.
Even had the notion of where have you been before or not
to not revisit places you’ve been before.
It still didn’t scale yet
to the real world kind of scenarios I think you had in mind,
but it was some sign of life
that maybe you can meta learn these hierarchical concepts.
I mean, it seems like through these meta learning concepts,
get at the, what I think is one of the hardest
and most important problems of AI,
which is transfer learning.
So it’s generalization.
How far along this journey
towards building general systems are we?
Being able to do transfer learning well.
So there’s some signs that you can generalize a little bit,
but do you think we’re on the right path
or it’s totally different breakthroughs are needed
to be able to transfer knowledge
between different learned models?
Yeah, I’m pretty torn on this in that
I think there are some very impressive.
Well, there’s just some very impressive results already.
I mean, I would say when,
even with the initial kind of big breakthrough in 2012
with AlexNet, the initial thing is okay, great.
This does better on ImageNet, hence image recognition.
But then immediately thereafter,
there was of course the notion that,
wow, what was learned on ImageNet
and you now wanna solve a new task,
you can fine tune AlexNet for new tasks.
And that was often found to be the even bigger deal
that you learn something that was reusable,
which was not often the case before.
Usually machine learning, you learn something
for one scenario and that was it.
And that’s really exciting.
I mean, that’s a huge application.
That’s probably the biggest success
of transfer learning today in terms of scope and impact.
That was a huge breakthrough.
And then recently, I feel like similar kind of,
by scaling things up, it seems like
this has been expanded upon.
Like people training even bigger networks,
they might transfer even better.
If you looked at, for example,
some of the OpenAI results on language models
and some of the recent Google results on language models,
they’re learned for just prediction
and then they get reused for other tasks.
And so I think there is something there
where somehow if you train a big enough model
on enough things, it seems to transfer
some deep mind results that I thought were very impressive,
the Unreal results, where it was learned to navigate mazes
in ways where it wasn’t just doing reinforcement learning,
but it had other objectives it was optimizing for.
So I think there’s a lot of interesting results already.
I think maybe where it’s hard to wrap my head around this,
to which extent or when do we call something generalization?
Or the levels of generalization in the real world,
or the levels of generalization involved
in these different tasks, right?
You draw this, by the way, just to frame things.
I’ve heard you say somewhere, it’s the difference
between learning to master versus learning to generalize,
that it’s a nice line to think about.
And I guess you’re saying that it’s a gray area
of what learning to master and learning to generalize,
where one starts.
I think I might have heard this.
I might have heard it somewhere else.
And I think it might’ve been one of your interviews,
maybe the one with Yoshua Benjamin, I’m not 100% sure.
But I liked the example, I’m not sure who it was,
but the example was essentially,
if you use current deep learning techniques,
what we’re doing to predict, let’s say,
the relative motion of our planets, it would do pretty well.
But then now if a massive new mass enters our solar system,
it would probably not predict what will happen, right?
And that’s a different kind of generalization.
That’s a generalization that relies
on the ultimate simplest, simplest explanation
that we have available today
to explain the motion of planets,
whereas just pattern recognition could predict
our current solar system motion pretty well, no problem.
And so I think that’s an example
of a kind of generalization that is a little different
from what we’ve achieved so far.
And it’s not clear if just regularizing more
and forcing it to come up with a simpler, simpler,
simpler explanation and say, look, this is not simple.
But that’s what physics researchers do, right?
They say, can I make this even simpler?
How simple can I get this?
What’s the simplest equation that can explain everything?
The master equation for the entire dynamics of the universe,
we haven’t really pushed that direction as hard
in deep learning, I would say.
Not sure if it should be pushed,
but it seems a kind of generalization you get from that
that you don’t get in our current methods so far.
So I just talked to Vladimir Vapnik, for example,
who’s a statistician of statistical learning,
and he kind of dreams of creating
the E equals MC squared for learning, right?
The general theory of learning.
Do you think that’s a fruitless pursuit
in the near term, within the next several decades?
I think that’s a really interesting pursuit
in the following sense, in that there is a lot of evidence
that the brain is pretty modular.
And so I wouldn’t maybe think of it as the theory,
maybe the underlying theory, but more kind of the principle
where there have been findings where
people who are blind will use the part of the brain
usually used for vision for other functions.
And even after some kind of,
if people get rewired in some way,
they might be able to reuse parts of their brain
for other functions.
And so what that suggests is some kind of modularity.
And I think it is a pretty natural thing to strive for
to see, can we find that modularity?
Can we find this thing?
Of course, every part of the brain is not exactly the same.
Not everything can be rewired arbitrarily.
But if you think of things like the neocortex,
which is a pretty big part of the brain,
that seems fairly modular from what the findings so far.
Can you design something equally modular?
And if you can just grow it,
it becomes more capable probably.
I think that would be the kind of interesting
underlying principle to shoot for that is not unrealistic.
Do you think you prefer math or empirical trial and error
for the discovery of the essence of what it means
to do something intelligent?
So reinforcement learning embodies both groups, right?
To prove that something converges, prove the bounds.
And then at the same time, a lot of those successes are,
well, let’s try this and see if it works.
So which do you gravitate towards?
How do you think of those two parts of your brain?
Maybe I would prefer we could make the progress
And the reason maybe I would prefer that is because often
if you have something you can mathematically formalize,
you can leapfrog a lot of experimentation.
And experimentation takes a long time to get through.
And a lot of trial and error,
kind of reinforcement learning, your research process,
but you need to do a lot of trial and error
before you get to a success.
So if you can leapfrog that, to my mind,
that’s what the math is about.
And hopefully once you do a bunch of experiments,
you start seeing a pattern.
You can do some derivations that leapfrog some experiments.
But I agree with you.
I mean, in practice, a lot of the progress has been such
that we have not been able to find the math
that allows you to leapfrog ahead.
And we are kind of making gradual progress
one step at a time, a new experiment here,
a new experiment there that gives us new insights
and gradually building up,
but not getting to something yet where we’re just,
okay, here’s an equation that now explains how,
you know, that would be,
have been two years of experimentation to get there,
but this tells us what the result’s going to be.
Unfortunately, not so much yet.
Not so much yet, but your hope is there.
In trying to teach robots or systems
to do everyday tasks or even in simulation,
what do you think you’re more excited about?
Imitation learning or self play?
So letting robots learn from humans
or letting robots plan their own
to try to figure out in their own way
and eventually play, eventually interact with humans
or solve whatever the problem is.
What’s the more exciting to you?
What’s more promising you think as a research direction?
So when we look at self play,
what’s so beautiful about it is goes back
to kind of the challenges in reinforcement learning.
So the challenge of reinforcement learning
is getting signal.
And if you don’t never succeed, you don’t get any signal.
In self play, you’re on both sides.
So one of you succeeds.
And the beauty is also one of you fails.
And so you see the contrast.
You see the one version of me that did better
than the other version.
So every time you play yourself, you get signal.
And so whenever you can turn something into self play,
you’re in a beautiful situation
where you can naturally learn much more quickly
than in most other reinforcement learning environments.
So I think if somehow we can turn more
reinforcement learning problems
into self play formulations,
that would go really, really far.
So far, self play has been largely around games
where there is natural opponents.
But if we could do self play for other things,
and let’s say, I don’t know,
a robot learns to build a house.
I mean, that’s a pretty advanced thing
to try to do for a robot,
but maybe it tries to build a hut or something.
If that can be done through self play,
it would learn a lot more quickly
if somebody can figure that out.
And I think that would be something
where it goes closer to kind of the mathematical leapfrogging
where somebody figures out a formalism to say,
okay, any RL problem by playing this and this idea,
you can turn it into a self play problem
where you get signal a lot more easily.
Reality is, many problems we don’t know
how to turn into self play.
And so either we need to provide detailed reward.
That doesn’t just reward for achieving a goal,
but rewards for making progress,
and that becomes time consuming.
And once you’re starting to do that,
let’s say you want a robot to do something,
you need to give all this detailed reward.
Well, why not just give a demonstration?
Because why not just show the robot?
And now the question is, how do you show the robot?
One way to show is to tally operate the robot,
and then the robot really experiences things.
And that’s nice, because that’s really high signal
to noise ratio data, and we’ve done a lot of that.
And you teach your robot skills in just 10 minutes,
you can teach your robot a new basic skill,
like okay, pick up the bottle, place it somewhere else.
That’s a skill, no matter where the bottle starts,
maybe it always goes onto a target or something.
That’s fairly easy to teach your robot with tally up.
Now, what’s even more interesting
if you can now teach your robot
through third person learning,
where the robot watches you do something
and doesn’t experience it, but just kind of watches you.
It doesn’t experience it, but just watches it
and says, okay, well, if you’re showing me that,
that means I should be doing this.
And I’m not gonna be using your hand,
because I don’t get to control your hand,
but I’m gonna use my hand, I do that mapping.
And so that’s where I think one of the big breakthroughs
has happened this year.
This was led by Chelsea Finn here.
It’s almost like learning a machine translation
for demonstrations, where you have a human demonstration,
and the robot learns to translate it
into what it means for the robot to do it.
And that was a meta learning formulation,
learn from one to get the other.
And that, I think, opens up a lot of opportunities
to learn a lot more quickly.
So my focus is on autonomous vehicles.
Do you think this approach of third person watching,
the autonomous driving is amenable
to this kind of approach?
So for autonomous driving,
I would say third person is slightly easier.
And the reason I’m gonna say it’s slightly easier
to do with third person is because
the car dynamics are very well understood.
Easier than first person, you mean?
Or easier than…
So I think the distinction between third person
and first person is not a very important distinction
for autonomous driving.
They’re very similar.
Because the distinction is really about
who turns the steering wheel.
Or maybe, let me put it differently.
How to get from a point where you are now
to a point, let’s say, a couple meters in front of you.
And that’s a problem that’s very well understood.
And that’s the only distinction
between third and first person there.
Whereas with the robot manipulation,
interaction forces are very complex.
And it’s still a very different thing.
For autonomous driving,
I think there is still the question,
imitation versus RL.
So imitation gives you a lot more signal.
I think where imitation is lacking
and needs some extra machinery is,
it doesn’t, in its normal format,
doesn’t think about goals or objectives.
And of course, there are versions of imitation learning
and versus reinforcement learning type imitation learning
which also thinks about goals.
I think then we’re getting much closer.
But I think it’s very hard to think of a
fully reactive car, generalizing well.
If it really doesn’t have a notion of objectives
to generalize well to the kind of general
that you would want.
You’d want more than just that reactivity
that you get from just behavioral cloning
slash supervised learning.
So a lot of the work,
whether it’s self play or even imitation learning,
would benefit significantly from simulation,
from effective simulation.
And you’re doing a lot of stuff
in the physical world and in simulation.
Do you have hope for greater and greater
power of simulation being boundless eventually
to where most of what we need to operate
in the physical world could be simulated
to a degree that’s directly transferable
to the physical world?
Or are we still very far away from that?
So I think we could even rephrase that question
in some sense.
And so the power of simulation, right?
As simulators get better and better,
of course, becomes stronger
and we can learn more in simulation.
But there’s also another version
which is where you say the simulator
doesn’t even have to be that precise.
As long as it’s somewhat representative
and instead of trying to get one simulator
that is sufficiently precise to learn in
and transfer really well to the real world,
I’m gonna build many simulators.
Ensemble of simulators?
Ensemble of simulators.
Not any single one of them is sufficiently representative
of the real world such that it would work
if you train in there.
But if you train in all of them,
then there is something that’s good in all of them.
The real world will just be another one of them
that’s not identical to any one of them
but just another one of them.
Another sample from the distribution of simulators.
We do live in a simulation,
so this is just one other one.
I’m not sure about that, but yeah.
It’s definitely a very advanced simulator if it is.
Yeah, it’s a pretty good one.
I’ve talked to Stuart Russell.
It’s something you think about a little bit too.
Of course, you’re really trying to build these systems,
but do you think about the future of AI?
A lot of people have concern about safety.
How do you think about AI safety?
As you build robots that are operating in the physical world,
what is, yeah, how do you approach this problem
in an engineering kind of way, in a systematic way?
So when a robot is doing things,
you kind of have a few notions of safety to worry about.
One is that the robot is physically strong
and of course could do a lot of damage.
Same for cars, which we can think of as robots too
in some way.
And this could be completely unintentional.
So it could be not the kind of longterm AI safety concerns
that, okay, AI is smarter than us and now what do we do?
But it could be just very practical.
Okay, this robot, if it makes a mistake,
what are the results going to be?
Of course, simulation comes in a lot there
to test in simulation. It’s a difficult question.
And I’m always wondering, like, I always wonder,
let’s say you look at, let’s go back to driving
because a lot of people know driving well, of course.
What do we do to test somebody for driving, right?
Get a driver’s license. What do they really do?
I mean, you fill out some tests and then you drive.
And I mean, it’s suburban California.
That driving test is just you drive around the block,
pull over, you do a stop sign successfully,
and then you pull over again and you’re pretty much done.
And you’re like, okay, if a self driving car did that,
would you trust it that it can drive?
And I’d be like, no, that’s not enough for me to trust it.
But somehow for humans, we’ve figured out
that somebody being able to do that is representative
of them being able to do a lot of other things.
And so I think somehow for humans,
we figured out representative tests
of what it means if you can do this, what you can really do.
Of course, testing humans,
humans don’t wanna be tested at all times.
Self driving cars or robots
could be tested more often probably.
You can have replicas that get tested
that are known to be identical
because they use the same neural net and so forth.
But still, I feel like we don’t have this kind of unit tests
or proper tests for robots.
And I think there’s something very interesting
to be thought about there,
especially as you update things.
Your software improves,
you have a better self driving car suite, you update it.
How do you know it’s indeed more capable on everything
than what you had before,
that you didn’t have any bad things creep into it?
So I think that’s a very interesting direction of research
that there is no real solution yet,
except that somehow for humans we do.
Because we say, okay, you have a driving test, you passed,
you can go on the road now,
and humans have accidents every like a million
or 10 million miles, something pretty phenomenal
compared to that short test that is being done.
So let me ask, you’ve mentioned that Andrew Ng by example
showed you the value of kindness.
Do you think the space of policies,
good policies for humans and for AI
is populated by policies that with kindness
or ones that are the opposite, exploitation, even evil?
So if you just look at the sea of policies
we operate under as human beings,
or if AI system had to operate in this real world,
do you think it’s really easy to find policies
that are full of kindness,
like we naturally fall into them?
Or is it like a very hard optimization problem?
I mean, there is kind of two optimizations
happening for humans, right?
So for humans, there’s kind of the very long term
optimization which evolution has done for us
and we’re kind of predisposed to like certain things.
And that’s in some sense what makes our learning easier
because I mean, we know things like pain
and hunger and thirst.
And the fact that we know about those
is not something that we were taught, that’s kind of innate.
When we’re hungry, we’re unhappy.
When we’re thirsty, we’re unhappy.
When we have pain, we’re unhappy.
And ultimately evolution built that into us
to think about those things.
And so I think there is a notion that
it seems somehow humans evolved in general
to prefer to get along in some ways,
but at the same time also to be very territorial
and kind of centric to their own tribe.
Like it seems like that’s the kind of space
we converged onto.
I mean, I’m not an expert in anthropology,
but it seems like we’re very kind of good
within our own tribe, but need to be taught
to be nice to other tribes.
Well, if you look at Steven Pinker,
he highlights this pretty nicely in
Better Angels of Our Nature,
where he talks about violence decreasing over time
So whatever tension, whatever teams we pick,
it seems that the long arc of history
goes towards us getting along more and more.
So. I hope so.
So do you think that, do you think it’s possible
to teach RL based robots this kind of kindness,
this kind of ability to interact with humans,
this kind of policy, even to, let me ask a fun one.
Do you think it’s possible to teach RL based robot
to love a human being and to inspire that human
to love the robot back?
So to like RL based algorithm that leads to a happy marriage.
That’s an interesting question.
Maybe I’ll answer it with another question, right?
Because I mean, but I’ll come back to it.
So another question you can have is okay.
I mean, how close does some people’s happiness get
from interacting with just a really nice dog?
Like, I mean, dogs, you come home,
that’s what dogs do.
They greet you, they’re excited,
makes you happy when you come home to your dog.
You’re just like, okay, this is exciting.
They’re always happy when I’m here.
And if they don’t greet you, cause maybe whatever,
your partner took them on a trip or something,
you might not be nearly as happy when you get home, right?
And so the kind of, it seems like the level of reasoning
a dog has is pretty sophisticated,
but then it’s still not yet at the level of human reasoning.
And so it seems like we don’t even need to achieve
human level reasoning to get like very strong affection
And so my thinking is why not, right?
Why couldn’t, with an AI, couldn’t we achieve
the kind of level of affection that humans feel
among each other or with friendly animals and so forth?
So question, is it a good thing for us or not?
That’s another thing, right?
Because I mean, but I don’t see why not.
Why not, yeah, so Elon Musk says love is the answer.
Maybe he should say love is the objective function
and then RL is the answer, right?
Oh, Peter, thank you so much.
I don’t want to take up more of your time.
Thank you so much for talking today.
Well, thanks for coming by.
Great to have you visit.