Anca Dragan is a professor at Berkeley, working on human-robot interaction -- algorithms that look beyond the robot's function in isolation, and generate robot behavior that accounts for interaction and coordination with human beings.

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OUTLINE:
0:00 - Introduction
2:26 - Interest in robotics
5:32 - Computer science
7:32 - Favorite robot
13:25 - How difficult is human-robot interaction?
32:01 - HRI application domains
34:24 - Optimizing the beliefs of humans
45:59 - Difficulty of driving when humans are involved
1:05:02 - Semi-autonomous driving
1:10:39 - How do we specify good rewards?
1:17:30 - Leaked information from human behavior
1:21:59 - Three laws of robotics
1:26:31 - Book recommendation
1:29:02 - If a doctor gave you 5 years to live...
1:32:48 - Small act of kindness
1:34:31 - Meaning of life

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The following is a conversation with Anka Jogan, a professor at Berkeley, working on human robot interaction, algorithms that look beyond the robot's function in isolation and generate robot behavior that accounts for interaction and coordination with human beings.

She also consults at Waymo, the autonomous vehicle company, but in this conversation, she's 100% wearing her Berkeley hat.

She is 1 of the most brilliant and fun roboticists in the world to talk with.

I had a tough and crazy day leading up to this conversation so I was a bit tired, even more so than usual.

But almost immediately as she walked in, her energy, passion, and excitement for human-robot interaction was contagious.

So I had a lot of fun and really enjoyed this conversation.

This is the Artificial Intelligence Podcast.

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And now here's my conversation with Enka Drogon.

When did you first fall in love with robotics?

And it was somewhat accidental actually, because I first started getting into programming when I was a kid and then into math and then into, I decided computer science was the thing I was going to do.

And then I applied to the robotics institute at Carnegie Mellon.

And I was coming from this little school in Germany that nobody had heard of, but I had spent an exchange semester at Carnegie Mellon.

So that was the only place, you know, MIT said no, Berkeley said no, Stanford said no.

So I went there to the robotics institute and I thought that robotics is a really cool way to actually apply the stuff that I knew and love, like optimization.

I have a better story how I got into cars, which is I used to do mostly manipulation in my PhD, but now I do kind of a bit of everything application wise, including cars.

And I got into cars because I was here in Berkeley while I was a PhD student still for RSS 2014, Peter Beal organized it.

And he arranged for it was Google at the time to give us rides and self-driving cars.

And I was in a robot and it was just making decision after decision the right call.

So it was a whole different experience, right?

Just, I mean, manipulation is so hard, you can't do anything and there it was.

Was that the most magical robot you've ever met?

So like for me to meet Google self-driving car for the first time was like a transformative moment.

Like I had 2 moments like that, that and Spot Mini.

I don't know if you met Spot Mini from Boston Dynamics.

Like, cause I know how Spot Mini works, right?

It's just, I mean, there's nothing truly special.

It's great engineering work, but the anthropomorphism that went on into my brain, that came to life, like it had a little arm and it like, and looked at me.

I don't know, there's a magical connection there.

And it made me realize, wow, robots can be so much more than things that manipulate objects.

They can be things that have a human connection.

Do you have, was the self-driving car the moment, like, was there a robot that truly sort of inspired you?

That was I remember that experience very viscerally riding in that car and being just wowed.

I I had the they gave us a sticker that said I rode in a self-driving car and it had this cute little firefly on it.

Oh, that was like the smaller 1, like the firefly.

And I put it on my laptop and I had that for years until I finally changed my laptop out.

What about if we walk back, you mentioned optimization.

Like what beautiful ideas inspired you in math, computer science early on?

Seems like a cold and boring field of math.

The thing is I liked math from very early on, from fifth grade is when I got into the math Olympiad and all of that.

Yeah, Romania is like our national sport, you got to understand.

So I got into that fairly early and it was a little maybe too just theory with no kind of, I didn't really have a goal.

And other than understanding, which was cool, I always liked learning and understanding, but there was no, okay, what am I applying this understanding to?

And so I think that's how I got into, more heavily into computer science, because it was kind of math meets something you can do tangibly in the world.

Do you remember like the first program you've written?

The first program I've written with, I kind of do, it was in QBasic in fourth grade.

That was, I don't know how to do that anymore.

But in fourth grade, that's the first thing that they taught me.

I was like, you could take a special, I wouldn't say it was an extra, it was in a sense an extra curricular so you could sign up for, you know, dance or music or programming.

Did you compete in program like these days?

Romania probably that's like a big thing.

I did a little bit of the computer science Olympian, but not as seriously as I did the math Olympian.

So it was programming, yeah, it's basically, here's a hard math problem, solve it with a computer, it was kind of the deal.

Oh yeah, it's more like algorithm testing.

So, again, you kind of mentioned the Google self-driving car, but outside of that, what's like who or what is your favorite robot?

Real or fictional that like captivated your imagination throughout?

I mean, I guess you kind of alluded to the Google search, right?

The firefly was a magical moment, but is there something else?

Okay, My favorite fictional robot is WALL-E.

I'm personally thinking a little bit about expressive motion kinds of things you were saying with you can do this and it's a head and it's the manipulator and what does it all mean.

So yeah, it goes woo and then it's super cute.

Yeah, the way it moves is just so expressive.

The timing of that motion, what it's doing with its arms and what it's doing with these lenses is amazing.

And so I've really liked that from the start.

And then on top of that, sometimes I shared this, it's a personal story I share with people or when I teach about AI or whatnot.

My husband proposed to me by building a WALL-E and he actuated it.

So it's 7 degrees of freedom, including the lens thing.

And it kind of came in and it had the, he made it have like a, you know, the belly box opening thing.

And then it spewed out this box made out of Legos that open slowly and then bam, yeah.

That could be like the most impressive thing I've ever heard.

Long story short, I like WALL-E because I like animation and I like robots and I like, you know, the fact that this was, we still have this robot to this day.

Do you think of the expressivity of robots?

Like the, with the Boston Dynamics, I never talked to those folks about this particular element.

I've talked to them a lot, but it seems to be like almost an accidental side effect for them that they weren't, I don't know if they're faking it, they weren't trying to, okay.

They do say that the gripper, it was not intended to be a face.

I don't know if that's a honest statement, but I think they're legitimate.

So do we automatically just anthropomorphize anything we can see about a robot?

So like The question is, how hard is it to create a WALL-E type robot that connects so deeply with us humans?

So if you want to do it in this very particular narrow setting where it does only 1 thing and it's expressive, then you can get an animator.

You can have Pixar on call, come in, design some trajectories.

Anki had a robot called Cosmo where they put in some of these animations.

The hard part is doing it not via these kind of handcrafted behaviors, but doing it generally autonomously.

I don't work on, just to clarify, I used to work a lot on this.

I don't work on that quite as much these days, but the notion of having robots that, you know, when they pick something up and put it in a place, they can do that with various forms of style, or you can say, well, this robot is, you know, succeeding at this task and is confident versus it's hesitant versus maybe it's happy or it's disappointed about something, some failure that it had.

I think that when robots move, they can communicate so much about internal states or perceived internal states that they have.

And I think that's really useful and an element that we'll want in the future because I was reading this article about how kids are being rude to Alexa because they can be rude to it and it doesn't really get angry, right?

So I think there's, at least for that, for the correct development of children, it's important that these things, you kind of react differently.

I also think that you walk in your home and you have a personal robot, and if you're really pissed, presumably the robot should kind of behave slightly differently than when you're super happy and excited.

But it's really hard because it's, I don't know, the way I would think about it and the way I've thought about it when it came to expressing goals or intentions for robots, it's, Well, what's really happening is that instead of doing robotics where you have your state and you have your action space and you have your space, the reward function that you're trying to optimize, now you kind of have to expand the notion of state to include this human internal state, what is the person actually perceiving?

What do they think about the robots, something or other?

And then you have to optimize in that system.

And so that means they have to understand how your motion, your actions end up sort of influencing the observer's kind of perception of you.

And it's very hard to write math about that.

Right, so when you start to think about incorporating the human into the state model, apologize for the philosophical question, but how complicated are human beings, do you think?

Like, can they be reduced to a kind of, almost like an object that moves and maybe has some basic intents?

Or is there something, do we have to model things like mood and general aggressiveness and time?

I mean, all these kinds of human qualities or like game theoretic qualities, like what's your sense?

is- How hard is the problem of human-robot interaction?

Should we talk about what the problem of human-robot interaction is?

And by the way, I'm going to talk about this very particular view of human-robot interaction, Which is not so much on the social side or on the side of how do you have a good conversation with the robot?

It turns out that if you make robots taller versus shorter, this has an effect on how people act with them.

But I'm talking about this very kind of narrow thing, which is you take, if you want to take a task that a robot can do in isolation, in a lab out there in the world, but in isolation.

And now you're asking, what does it mean for the robot to be able to do this task for presumably what it's actually end goal is, which is to help some person.

That ends up changing the problem in 2 ways.

The first way it changes the problem is that the robot is no longer the single agent acting.

Do you have humans who also take actions in that same space?

You know, cars navigating around people, robots around an office, navigating around the people in that office.

If I send the robot over there in the cafeteria to get me a coffee, then there's probably other people reaching for stuff in the same space.

And so now you have your robot and you're in charge of the actions that the robot is taking.

Then you have these people who are also making decisions and taking actions in that same space.

And even if the robot knows what it should do and all of that, just coexisting with these people, getting the actions to gel well, to mesh well together, that's the problem number 1.

And then there's problem number 2, which goes back to this notion of, if I'm a programmer, I can specify some objective for the robot to go off and optimize and specify the task.

But if I put the robot in your home, presumably you might have your own opinions about, well, okay, I want my house clean, but how do I want it clean?

Then how should robot, how close to me it should come and all of that.

And so I think those are the 2 differences that you have.

You're acting around people and what you should be optimizing for should satisfy the preferences of that end user, not of your programmer who programmed you.

Yeah, and the preferences thing is tricky.

So figuring out those preferences, be able to interactively adjust, to understand what the human is doing.

So really it boils down to understand the humans in order to interact with them and in order to please them.

So I think there's 2 tasks about understanding humans that in my mind are very, very similar, but Not everyone agrees.

So there's the task of being able to just anticipate what people will do.

We all know that cards need to do this, right?

We all know that, well, if I navigate around some people, the robot has to get some notion of, okay, where is this person gonna be?

And then there's what you were saying, satisfying the preferences, right?

So adapting to the person's preferences, knowing what to optimize for, which is more this inference side, this what does this person want?

And to me, those kind of go together because I think that at the very least, if you can understand, if you can look at human behavior and understand what it is that they want, then that's sort of the key enabler to being able to anticipate what they'll do in the future.

Because I think that, you know, we're not arbitrary.

We make these decisions that we make, we act in the way we do, because we're trying to achieve certain things.

And so I think that's the relationship between them.

Now, how complicated do these models need to be in order to be able to understand what people want?

So we've gotten a long way in robotics with something called inverse reinforcement learning, which is the notion of someone acts, demonstrates how they want the thing done.

Right, so it's the problem of take human behavior and infer reward function from this.

Figure out what it is that that behavior is optimal with respect to.

And it's a great way to think about learning human preferences in the sense of, you know, you have a car and the person can drive it, and then you can say, well, okay, I can actually learn what the person is optimizing for.

I can learn their driving style, or you can have people demonstrate how they want the house clean.

And then you can say, okay, I'm getting the trade-offs that they're making.

I'm getting the preferences that they want out of this.

And so we've been successful in robotics somewhat with this.

And it's based on a very simple model of human behavior.

It was remarkably simple, which is that human behavior is optimal with respect to whatever it is that people want, right?

So you make that assumption and now you can kind of inverse through.

That's why it's called inverse, well, really optimal control, but also inverse reinforcement learning.

So this is based on utility maximization in economics.

Right, so back in the 40s, von Neumann and Morgenstein were like, okay, people are making choices by maximizing utility, go.

And then in the late 50s, we had Luce and Shepard come in and say, people are a little bit noisy and approximate in that process.

So they might choose something kind of stochastically with probability proportional to how much utility something has.

This has translated into robotics and something that we call Boltzmann rationality.

So it's a kind of an evolution of inverse reinforcement learning that accounts for human noise.

And we've had some success with that too for these tasks where it turns out people act noisily enough that you can't just do vanilla, the vanilla version.

You can account for noise and still infer what they seem to want based on this.

Then now we're hitting tasks where that's not enough.

So imagine you're trying to control some robot that's fairly complicated.

You're trying to control a robot arm because maybe you're a patient with a motor impairment and you have this wheelchair-mounted arm and you're trying to control it around.

Or 1 test that we've looked at with Sergei is, and our students did, is a lunar lander.

So, I don't know if you know this Atari game, it's called Lunar Lander.

Mostly they just crash it left and right.

Okay, so this is the kind of task where imagine you're trying to provide some assistance to a person operating such a robot where you want the autonomy to kick in, figure out what it is that you're trying to do and help you do it.

It's really hard to do that for, say, lunar lander because people are all over the place.

And so they seem much more noisy than really rational.

That's an example of a task where these models are kind of failing us.

And it's not surprising because, so we talked about the 40s utility, late 50s, sort of noisy.

Then the 70s came and behavioral economics started being a thing where people were like, no, no, no, no, no, people are not rational.

People are messy and emotional and irrational and have all sorts of heuristics that might be domain specific and they're just a mess.

So what does my robot do to understand what you want?

For the most part, we get away with pretty simple models until we don't.

And then the question is, what do you do then?

And I had days when I wanted to, you know, pack my bags and go home and switch jobs because it's just, it feels really daunting to make sense of human behavior enough that you can reliably understand what people want, especially as, you know, robot capabilities will continue to get developed.

You'll get these systems that are more and more capable of all sorts of things.

And then you really want to make sure that you're telling them the right thing to do.

So if I just sat here quietly and tried to understand something about you by listening to you talk, it would be harder than if I got to say something and ask you and interact and control.

Can the robot help its understanding of the human by influencing the behavior by actually acting?

So 1 of the things that's been exciting to me lately is this notion that when you try to, that When you try to think of the robotics problem as, okay, I have a robot and it needs to optimize for whatever it is that a person wants it to optimize, as opposed to maybe what a programmer said, that problem we think of as a human-robot collaboration problem in which both agents get to act, in which the robot knows less than the human because the human actually has access to, at least implicitly, to what it is that they want.

They can't write it down, but they can talk about it.

They can give all sorts of signals, they can demonstrate.

And, but the robot doesn't need to sit there and passively observe human behavior and try to make sense of it.

And so there's these information gathering actions that the robot can take to sort of solicit responses that are actually informative.

So for instance, this is not for the purpose of assisting people, but with kind of back to coordinating with people in cars and all of that.

1 thing that Dorsa did was, so we were looking at cars being able to navigate around people, and you might not know exactly the driving style of a particular individual that's next to you, but you wanna change lanes in front of them.

Navigating around other humans inside cars?

So if you have an autonomous car and it's trying to navigate the road around human-driven vehicles.

Similar things, ideas apply to pedestrians as well, but let's just take human-driven vehicles.

Well, you could be trying to infer the driving style of this person next to you.

You'd like to know if they're in particular, if they're sort of aggressive or defensive, if they're going to let you kind of go in or if they're going to not.

And it's very difficult to just, you know, if you think that if you want to hedge your bets and say, oh, maybe they're actually pretty aggressive, I shouldn't try this.

You kind of end up driving next to them and driving next to them, right?

And then you don't know because you're not actually getting the observations that you get the way someone drives when they're next to you and they just need to go straight.

It's kind of the same regardless if they're aggressive or defensive.

And so you need to enable the robot to reason about how it might actually be able to gather information by changing the actions that it's taking.

And then the robot comes up with these cool things where it kind of nudges towards you and then sees if you're gonna slow down or not.

Then if you slow down, it sort of updates its model of you and says, oh, okay, you're more on the defensive side.

That's so cool that you could use your own actions to gather information.

That feels like a totally open, exciting new world of robotics.

I mean, how many people are even thinking about that kind of thing?

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Anca Dragan: Human-Robot Interaction and Reward Engineering | Lex Fridman Podcast #81