Kind: captions
Language: en
the following is a conversation with
ANCA Jorgen a professor of Berkeley
working on human robot interaction
algorithms and looked beyond the robots
function in isolation and generate robot
behavior that accounts for interaction
and coordination with human beings
she also consults at way Moe the
autonomous vehicle company but in this
conversation she's 100% wearing her
Berkeley hat she is one 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 enjoy this
conversation this is the artificial
intelligence podcast if you enjoy it
subscribe I need to review it with five
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advanced robotics and STEM education for
young people around the world and now
here's my conversation with Enka Droog
on
when did you first fall in love with
robotics I think it was a very gradual
process and it was somewhat accidental
actually because I first started getting
into programming when I was a kid and
then into Mass and then into compute I
disliked computer science was the thing
I was gonna do and then in college I got
into AI and then I applied to the
robotics Institute at Carnegie Mellon
and I was coming from this little school
and Germany didn't know any nobody had
heard of but I had spent an exchange
semester at Carnegie Mellon so I had
letters from Carnegie Mellon so that was
the only play you know I might he said
no Berkeley said no Stanford said no
that was the only place I got into so I
went there it's a robotics Institute and
I thought that robotics is a really cool
way to actually apply the stuff that I
knew and loved like optimization so
that's how I got into robotics I have a
better story how I got into cars which
is I you know 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
better be organized in and he arranged
for it was Google at a 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 and he was
so amazing so it was a whole different
experience right it's just I mean
manipulation is so hard you can't do
anything and there was was it the most
magical robot you've ever met so like
for me to mean Google self-driving car
for the first time was like a
transformative moment the guy had two
moments like that that and spot mini I
don't know if you met Bob many for
Boston Dynamics I felt like I felt like
I fell in love or something like it
because I thought I know how a spot many
works right it's just I mean there's
nothing truly special it is it's great
engineering work but the
anthropomorphism that went on into my
brain
they came to life like a head little arm
and like and looked at me he she looked
at me you know 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
Jeff was a self-driving car the moment
like it 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
wrote in a self-driving car and I had
this cute little Firefly on yes and our
logo that was like the smaller were like
you had the really cute one yeah and and
I put it on my laptop and I had that for
years until I finally changed my laptop
out and you know what about if we walk
back you mention optimization at like
what beautiful ideas inspired you in
math computer science early on like why
get into this field seems like a cold
and boring field of math like what was
exciting to you about it the thing is I
liked math from very early on from fifth
grade is when I got into the math
Olympia and all of that are you competed
yeah this it Romania is like our
national sport do you speak I understand
so I got into that fairly early and and
it was little maybe to just theory with
no kind of I didn't kind of how - didn't
really have a goal and I didn't
understanding which was cool I always
liked learning and understanding but
there was no can what am i applying this
understanding to and so I think that's
how I got into more heavily into
computer science cuz it was it was kind
of math meets something you can do
tangibly in the world do you remember
like the first program you've written
okay the first program I've written with
I kind of do it wasn't cute basic and
fourth grade and it was drawing like a
circle right yeah you know I don't know
how to do that anymore
right that's like the first thing that
they taught me I was like you could take
a special
I wouldn't say was an extra isn't a
sense an extracurricular so you could
sign up for you know dance or music or
programming and I did the programming
thing and I was like what what I know
why did you compete in program like
these days Romania probably that's like
a big thing there's a program of
competition hmm what was that did that
touch you at all did a little bit of the
computer science Olympian but not not as
seriously as I did the math Olympiad so
is programming yeah it's basically
here's a hard math problem solve it with
a computer it was kind of yeah it's more
like algorithms exactly it's not where's
algorithmic so okay you kind of
mentioned the Google self-driving car
but outside of that Oh what's like who
or what is your favorite robot real or
fictional that I captivated your
imagination throughout I mean I guess
you kind of alluded to the Google
self-driving the Firefly was a magical
moment but is there something else it
was I think there was the Lexus by the
way this was back back then but yeah so
good question I am okay my favorite
fictional robot is Wally and I love how
amazingly expressive it is some personal
things a little bit about expressive
motion kinds of things you were staying
with you can do this and it's a head and
it's a manipulator and what does it all
mean I like to think about that stuff I
love Pixar
I love animation I love Wally has two
big eyes I think or no yeah it has these
um these cameras and they move so yeah
that's it so you know it goes through
and then it's super cute
it's yeah I think you know the way it
moves it's just so expressive the timing
of that motion what is doing with its
arms and what it's doing with these
lenses is amazing and so I've 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 Wally and he actuated it
so it's seven 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 so it just did that and then it's
filled out this box made out of Legos
that open slowly and then BAM no yeah
yeah it was it was quite quite it's at a
bar it could be like the most impressive
thing I've ever heard
okay special connection to Wally long
story short I like Wally because I like
animation and I like robots and I like
you know the fact that this was I we
still have this robot to this day what
how hard is that problem 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 him 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 the gripper on it was not intended
to be a face I don't know if that's a
honest statement but I think they're
legitimate and so do we automatically
just anthropomorphizing and youths up
anything we could see about a robot it's
like the the question is how hard is it
to create a wall-e type robot that
connects so deeply with us humans what
do you think it's really hard right so
it depends on what settings so if you
want to do it in this very particular
narrow setting where it does only one
thing and it's expressive then you can
get an animator you know can have fixer
on call come in design some trajectory
is there was a a key had a robot called
Cosmo
where they put in some of these
animations that part is easy right the
hard part is doing it not via these kind
of handcrafted behaviors but doing it
generally autonomously like I want robot
say I don't work on just to clarify I
don't I used to work a lot on this I
don't work on that quite as much these
days but
but have 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 desk and is confident
versus its hesitant versus you know
maybe it's happy or it's you know
disappointed about something some
failure that it had or 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 in an element
that we'll want in the future because I
was reading this article about how kids
are kids are being rude to Alexa because
they can be rude to it and it doesn't
really get angry right it doesn't reply
it in any way it just says the same
thing so I think there's at least for
that for the for the correct development
of children to learn that these things
and you kind of react differently I also
think you know you walk in your home and
you have a personal robot and if you're
really pissed presumably robot just kind
of behave slightly differently than one
you're super happy and excited but it's
really hard because it's I don't know I
don't you know the way I would think
about it and the way I've thought about
it when it came to in expressing goals
or intent its our 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
functions are 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's better and then you have to
optimize in that system and so that
means you have to understand how your
motion your actions end up sort of
influencing the observers kind of
perception of you and it's very 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
two 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 how complicated it is how hard is
the problem of human robot interaction
yeah should we talk about what the
problem of human robot is yeah this is
what I mean talk about how that yeah so
and by the way I'm gonna talk about this
very particular view of human robot
interaction right which is not so much
on the social side or on the side of how
do you have a good conversation with the
robot what should the robots appearance
be throws out that if you make robots
taller versus shorter this has an effect
on how people act with them so I'm not
I'm not talking about that 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 angola's
which is to help some person that ends
up changing the problem in two ways the
first way to changes the problem is that
the robot is no longer the single agent
acting there 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 to over there in the cafeteria to
get me a coffee
then there's from 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 you know the
robot knows what it's what it should do
and all of that just coexisting with
these people right kind of getting the
since the gel well to mesh well together
that sort of the problem number one and
then there's problem number two which is
goes back to this notion of I if I'm a
programmer I can specify some objective
for the robot to go off and optimize you
can 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 two differences
that you have your acting around people
and you 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
so really boys ought to be understand
the humans in order to interact with
them in order to please them right so
why is this hard what yeah why is
understanding humans hard so I think
there's two 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 ok where where is this person
gonna be so that's kind of the
prediction side and then there's what
what you are saying satisfying the
preferences right so adapting to the
person's preference is knowing what to
optimize for which is more this
inference side this what is what does
this person want what is their intent
what are their preferences and to me
those kind of go together because I
think that in if you at very least if
you can understand if you 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
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
what how they want this thing done what
isn't inverse reinforcement learning you
said it right so it's 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 you can
have people demonstrate how they want
the house clean and then you can say
okay this is this is I mean I'm getting
the trade-offs that they're 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 a it's based on a very
simple model of human behavior which is
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 but also inverse
reinforcement learning so this is based
on utility maximization in economics
press back in the forties fine women
mortgage time or like okay people are
making choices by maximizing utility go
and then in the late 50s we had loose
and Shephard come in and say people are
a little bit noisy and approximate in
that process so they might choose
something kind of stochastic lee with
probability proportional to how much
utility something has there's a bit of
noise in there on this has translated
into
buttocks and something that we call
Boltzmann rationality so it's a kind of
an evolution of inversed reinforcement
learning that accounts for four 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 ah you can account for noise and
still infer what what they seem to want
based on this man now we're hitting
tasks word that's no not enough and what
are examples where are you damn desk so
imagine you're trying to control some
robot that's that's fairly complicated
trying to control the robot arm cuz
maybe you're a patient with a motor
impairment and you have this wheelchair
mounted army in China to control it
around or one test that we've looked at
with Sergei is and our students did is a
lunar lander so just I don't know if you
know this Atari game it's called lunar
lander it's it's really hard people
really suck at landing the same mostly
they just crash it left and right okay
so this is the kind of toss for imagine
you're trying to provide some assistance
to a person operating such such a robot
where you won the kind of the autonomy
to kick can 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 irrational that's an
example of a task where these models are
kind of failing us and it's not
surprising because so we you know we
talk about a 40s utility late fifties
sort of noisy then the seventies came
and behavioral economics started being a
thing where people are 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
they're just a messy mess so so what do
you so what does my robot do to
understand what you won and it's a very
it's very that's why it's complicated
it's you know 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
um and it I had days when I wanted to
you know pack my bags and go home and
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 what is that thing
well read it in human behavior so if I
just sit here quietly and try to
understand something about you but
listening to you talk it would be harder
than if I got to say something and ask
you and interact and control okay can
you can the robot help its understanding
of the human by inflowing it influencing
the behavior by actually acting yeah
absolutely so one of the things that's
been exciting to me lately is this
notion that when you tried to that that
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 and you
know at least implicitly to what it is
that they want they can't write it down
but they can they can talk about it they
can give all sorts of signals they can
demonstrate and and but the robot
doesn't need to sit there and passively
observe human behavior and try to make
sense of it the robot can act too 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
one 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 want to change lanes in front of
them navigating around other humans
inside cars yeah good good clarification
question so you have an autonomous car
and it's trying to navigate the road
around human driven vehicles similar
things ideas applied to pedestrians as
well but let's just take human driven
vehicles so now you're trying to change
a lane 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 gonna let you
kind of go in or if they're gonna not
and and it's very difficult to just you
know when if you think that if you want
to hedge your bets that maybe they're
actually pretty aggressive I shouldn't
ride this you kind of end up driving
next to them and driving next to them
right and then you you don't know
because you're not actually getting the
observations that you get away someone
drives when they're next to you and they
just need to go straight it's kind of
the same because if they're aggressive
or defensive and so you need to enable
the robot the 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 not just
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 so now
I can actually that's a fascinating
dance as so that's so cool
you could use your own actions to gather
information that's uh that feels like
I'm totally open exciting new world of
robotics prop I mean how many people are
even thinking about that kind of thing
because it's it's actually leveraging
human I mean most roboticist I've talked
to a lot of you know colleagues and so
on are kind of being honest kind of
afraid of humans because they're messy
and complicated right I understand
um going back to what we're talking
about earlier right now we're kind of in
this dilemma
okay there are tasks that we can just
assume people are approximately rational
for and we can figure out what they want
we can figure out their goals in fear
are their driving styles whatever cool
they're these tasks that we can't so
what do we do right do we pack our bags
and go home and this one is just I've
had a little bit of hope recently um and
I'm kind of doubting myself scoff what
do I know that you know 50 years of
behavioral economics hasn't figured out
but maybe it's not really in
contradiction with what with the way
that field is headed but basically one
thing that we've been thinking about is
instead of kind of giving up and saying
people are too crazy and irrational for
us to make sense of them maybe we can
give them a bit the benefit of the doubt
and maybe we can think of them as
actually being relatively rational but
just under different assumptions about
the world about how the world works
about you know they don't have we when
we think about rationality and bliss the
assumption is or they're rational under
all the same assumptions and constraints
as the robot right what if this is the
state of the world that's what they know
this is the transition function that's
what they know this is the horizon
that's what they know but maybe maybe
the kind of this difference the way the
reason they can seem a little messy and
hectic especially to robots is that
perhaps they just make different
assumptions or have different beliefs so
I mean that's that's another fascinating
idea that this are kind of anecdotal
desire to say that humans are irrational
perhaps grounded behavioral economics is
is that we just don't understand the
constraints and their awards under which
they operate and so our goal shouldn't
be to throw our hands up and say they're
irrational is to say let's try to
understand what are the constraints what
it is that there must be assuming that
makes this behavior make sense good life
lesson right good life that's true it's
just outside a robot is good too that's
communicating with humans that's just a
good assume that you just don't have
empathy right it's uh this is maybe
there
something you're missing and you know
and it's you know it especially happens
to robots because they're kind of dumb
and they don't know things and
oftentimes people are sort of super
irrational and that they actually know a
lot of things that robots don't
sometimes like with the lunar lander the
robot you know knows much more so it
turns out that if you try to say look
maybe people are operating this thing
but assuming a much more simple fight
physics model because they don't get the
complexity of this kind of craft or the
robot arm with seven degrees of freedom
when these inertia and whatever so so
maybe they have this intuitive physics
model which is not you know this notion
of intuitive physics is something that
good you just studied actually in
cognitive science was like Josh
Tenenbaum Tom Griffiths what kind of
stuff and and what we found is that you
can actually try to figure out what what
physics model kind of best explains
human actions and then you can use that
to sort of correct what it is that
they're commanding the craft to do so
they might you know be sending the craft
somewhere but instead of executing that
action you can sort of take a step back
and say according to their intuitive if
the world worked according their
intuitive physics model where do they
think that the craft is going war day
where are they trying to send it to and
then you can use the real physics right
the universe of that to actually figure
out what you should do so that you do
that instead of where they were actually
sending you in the real world and I kid
you not it word peopled landed there the
damn thing and you know in between the
two flags and and and all that so it's
not conclusive in any way but I'd say
it's evidence that
yeah maybe we're kind of under
estimating humans in some ways when
we're giving up and saying oh there's
just crazy noisy then you then you try
to explicitly try to model the kind of
worldview that they that they have
that's right that's right it's not to I
mean there's things to be here for
Konami's through that that that for
instance I've touched upon the planning
horizon so there's this idea that I just
bounded rationality essentially and the
idea that well maybe we work under
computational constraints and I think
kind of our view recently has been take
the bellmen update
nai and just break it in all sorts of
ways by saying state no no no the person
doesn't get to see the real state maybe
they're estimating somehow transition
function no no no no even the actual
reward evaluation maybe they're still
learning about what it is that they want
like like you know when you watch
netflix and you know you have all the
things and then you have to pick
something imagine that you know the D
the AI system interpreted that choice as
this is the thing you prefer to see and
how are you gonna know you're still
trying to figure out what you like what
you don't like etc so I mean it's
important to also account for that so
it's not irrationality precise doing the
right thing under the things that they
know yeah that's brilliant
you mentioned recommender systems what
kind of and we're talking about human
robot interaction kind of problem spaces
are you thinking about so is it robots
like wheeled robots of autonomous
vehicles is it object manipulation like
when you think about human robot
interaction in your mind and maybe I'm
tree could speak for the entire
community of human robot interaction no
but like what are the problems of
interest here is and does it you know I
kind of think of open domain dialogue as
human robot interaction and that happens
not in the physical space but it could
just happen in in the virtual space so
word who wears the boundaries of this
field for you when you're thinking about
the things we've been talking about yeah
so I I tried to find kind of underlying
I don't know what to even call them I
get try to work on you know I might call
what I do the kind of working on the
foundations of algorithmic human robot
interaction and trying to make
contributions there and and it's
important to me that whatever we do is
actually somewhat domain agnostic when
it comes to is it about you know
autonomous cars or is it about
quadrotors or is it a basis or the same
underlying principles apply of course
when you're trying to get a particular
to work usually have to do some extra
work to adapt that to that particular
domain but these things that we were
talking about around well you know how
do you model humans it turns out that a
lot of systems need to quote benefit
from a better understanding of how human
behavior relates to what people want and
need to predict human behavior physical
robots of all sorts and and beyond that
and so I used to do manipulation I used
to be you know picking up stuff and then
I was picking up stuff with people
around and now it's sort of very broad
when it comes to the application level
but in a sense very focused on ok how
does the problem need to change how do
the algorithms need to change when we're
not doing a robot by itself you know
emptying the dishwasher but we're
stepping outside of that oh I thought
that popped into my head just now on the
game theoretic side I think you said
this really interesting idea of using
actions to gain more information but if
we think a sort of game theory the
humans that are interacting with you
with you the robot identity of the robot
yeah is they also have a world model of
you mm-hmm
and you can manipulate that and if we
look at autonomous vehicles people have
a certain viewpoint you said with the
kids
people see Alexa as a in a certain way
is there some value in trying to also
optimize how people see you as a robot
is that it or is that a little too far
and away from the specifics of what we
can solve right now so both right so
it's really interesting and we've seen a
little bit of progress on this problem
on pieces of this problem so you can
again it kind of comes down to how
complicated is the human model need to
be but in one piece of work that we were
looking at we just said ok there's these
in there's this
that are internal to the robot and their
what their what the robot is about to do
or maybe what objective what driving
style the robot has or something like
that and what we're gonna do is we're
going to set up a system where part of
the state is the person's belief over
those parameters and now when the robot
acts that the person gets new evidence
about this robot internal state and so
they're updating their mental model of
the robot right so if they see a card
that sort of cut someone off Tory god
that's an aggressive card they no more
right if they see sort of a robot head
towards a particular door they're like
are the robots trying to get to that
door so this thing that we have to do
with humans to try to understand their
goals and intentions humans are
inevitably gonna do that to robots and
then that raises this interesting
question that you asked which is can we
do something about that this is gonna
happen inevitably but we can sort of be
more confusing or less confusing to
people and it turns out you can optimize
for being more informative and less
confusing if you if you have an
understanding of how your actions are
being interpreted by the human how
they're using these actions to update
their belief and honesty all we did is
just Bayes rule basically okay first has
a belief they see an action they make
some assumptions about how the robot
generates its actions presumably is
being rational because robots are
rational see reasonable to assume that
about them and then they incorporate
that that new piece of evidence the
Bayesian sense and their belief and they
obtain a posterior and now the robot is
trying to figure out what actions to
take such that it steers the person's
belief to put as much probability mass
as possible on the correct on the
correct parameters so that's kind of a
mathematical formalization of that but
my worry and I don't know if you want to
go there with me but I about this quite
a bit um the the kids talking to alexa
disrespectfully worries me i worry in
general about human nature I guess I
grew up in Soviet Union World War two
I'm gonna do two so with the Holocaust
and everything I just worry about how we
sometimes treat the other the the group
that we call out or whatever it is
through human history the group that's
the other has been changed faces but it
seems like the robot will be the other
the other the the next the other and one
thing is it feels to me that robots
don't get no respect they get shoved
around shoved around in is there one at
the shallow level for a better
experience it seems that robots need to
talk back a little bit like into my
intuition says I mean most companies
from sort of Roomba autonomous vehicle
companies might not be so happy with the
idea that a robot has a little bit of an
attitude but I feel it feels to me that
that's necessary to create a compelling
experience like we humans don't seem to
respect anything that doesn't give us
some attitude that or like Miss mix of
mystery and attitude and anger and did
that threatens us subtly maybe
passive-aggressively I don't it seems
like we humans yet need that dude what
are you is there something you have
thoughts on this one is one is it it's
we respond to you know someone being
assertive but we also respond to someone
being vulnerable so I think robots but
my first thought is that robots get
shoved around and and bullied a lot
because they're sort of you know
tempting and they're so showing off or
they appear to be showing off and so I
think current going back to these things
we were talking about in the beginning
of making robots a little more a little
more expressive a little bit more like
oh that wasn't cool to do and now I'm
bummed right I think that that can
actually help because people can't help
but anthropomorphize and respond to that
even that though the emotion being
communicate is not in any way a real
thing and people know that it's not a
real T because they know it's just a
machine
we're still interpret you know we can
work with we watch there's this a famous
psychology experiment with little
triangles and kind of dots on a screen
and a triangle is chasing the square and
get
angry at the darn triangle because why
is it not leaving the square alone so
that's yeah we can't helps that was the
first thought the vulnerability is
really interesting that I I think of
like being pushing back being assertive
as the only mechanism of getting of
forming a connection of gaining respect
but perhaps vulnerability perhaps
there's other mechanisms that are less
threatening yeah a little bit yes but
then this this other thing that we can
think about is it goes back to what you
were saying that interaction is really
game theoretic all right so the moment
you're taking actions in the space
humans are taking actions in that same
space but you have your own objective
which is you know you're a car you need
to get your passenger to the destination
and then the human nearby has their own
objective which someone overlaps with
you but not entirely you boat you're not
interested in getting into an accident
with each other but you have different
destinations and you want to get home
faster and they want to get home faster
and that's a general of some game at
that point and so that's I think that's
what it's reading it as such is kind of
a way we can step outside of this kind
of mode that where you try to anticipate
what people do and you don't realize you
have any influence over it while still
protecting yourself because your
understanding that people also
understand that they can influence you
and it's just kind of back and forth is
this negotiation which is really really
talking about different equilibria of a
game the very basic way to solve
coordination is to just make predictions
about what people will do and then stay
out of their way and that's hard for the
reasons we talked about which is how you
have to understand people's intentions
implicitly explicitly who knows but
somehow you have to get enough of an
understanding of that we all anticipate
what happens next and so that's
challenging but then it's further
challenged by the fact that people
change what they're do based on what you
do because they don't they don't plan in
isolation either right so when you see
cars trying to merge on a highway
and not succeeding one of the reasons
this can be is because you you they they
look at traffic that keeps coming they
predict what these people are planning
on doing which is to just keep going and
then they stay out of the way because
there's not there's no feasible plan
right any planning would actually
intersect with one of these other people
so that's bad so you get stuck there
so now kind of if if you start thinking
about it as no no no actually these
people change what they do depending on
what the car does like if the car
actually tries to kind of inch itself
forward they might actually slow down
and let the car in and down take an
advantage of that well that you know
that's kind of the next level we call
this like this under actuated system
idea where it's gonna under actresses
and robotics but it's kind of it's you
don't your influence these other degrees
of freedom but you don't get to decide
what somewhere it's seen you mention it
this the the human element in this
picture as under actuate it said you
know you understand under actuator about
robotics is you know that you can't
fully control the system so you can't go
in arbitrary directions in the
configuration space under your control
yeah it's a very simple way of under
actuation where basically there's
literally these degrees of freedom that
you can control and these are affirmed
that you can't but you influence them
and I think that's the important part is
that they don't do whatever regardless
of what you do that what you do
influence is what they end up doing I
just also like the the poetry of calling
human robot interaction and under
actuated robotics problem and y'all so
much sort of nudging it seems that there
and I don't know I think about this a
lot in the case of pedestrians I've
collected hundreds of hours of videos I
like to just watch pedestrians mmm-hmm
and it seems that it's a funny hobby
yeah it's weird because I learn a lot I
learned a lot about myself about our
human human behavior from watching
pedestrians watching people in their
environment basically crossing the
street is
you're putting your life on the line you
know I don't know tens of millions of
time in America every day is people are
just like playing this weird game of
chicken when they cross the street
especially when there's some ambiguity
about the right-of-way that has to do
either with the rules of the road or
with the general personality of the
intersection based on the time of day
and so on I mean and this nudging idea I
don't you know it seems that people
don't even nudge they just aggressively
take make a decision somebody there's a
runner that gave me this advice I
sometimes run in in the street and you
know not in this jannah sidewalk and you
said that if you don't make eye contact
with people when you're running they
will all move out of your way it's
called civil and attention civil
inattention that's the thing oh wow I
need to look this stuff but it works
what is that my sense was if you
communicate like confidence in your
actions that you're unlikely to deviate
from the action that you're following
that's a really powerful signal to
others that they need to plan around
your actions as opposed to nudging where
you're sort of hesitantly then the
hesitation might communicate that you're
now you're still in the dance in the
game that they can influence with their
own actions I've recently had
conversation with Jim Keller who is a
sort of this legendary chip or chip
architect but he also let the autopilot
in for a while and his intuition that
driving is fundamentally still like a
ballistics problem like you can ignore
the human element that it's just not
hitting things and you can kind of learn
the right dynamics required to do the
merger and all those kinds of things and
then my sense is and I don't know if I
can provide a definitive proof of this
but my sense is I can order a magnitude
or more more difficult when humans are
involved like it's not simply a object a
collision avoidance problem which where
does your intuition of course nobody
knows the right answer here but
where does your intuition fall on the
difficulty fundamental difficulty of the
driving problem when humans are involved
yeah good question I have many opinions
on this
imagine downtown San Francisco yeah yeah
it's crazy busy everything okay now take
all the humans out no pedestrians no
human driven vehicles no cyclists no
people and little skill electric
scooters have been around nothing I
think we're done I think driving at that
point is done we're done I did nothing
really that's nice tilt needs to be
solved about that well let's pause there
i I think I agree with you that guy and
I think a lot of people here will agree
with that but we need to sort of
internalize that idea so what's the
problem there because we're not quite
yet be done with that because a lot of
people kind of focus on the perception
problem well a lot of people kind of map
autonomous driving into how close are we
to solving being able to detect all the
you know the the drivable area the
objects in the scene do you see that as
a how hard is that problem so your
intuition there behind your statement
was we might have not solved the yet but
were close to solving basically the
perceptual problem I think the
perception problem I mean and by the way
a bunch of years ago this would not have
been true and a lot of issues and the
space can't we're coming from the fact
that we don't really you know we don't
know what's what's where but I think
it's fairly safe to say that at this
point although you could always improve
on things and all of that you can drive
through downtown San Francisco if there
are no people around there's no really
perception issue standing in your way
there any perception is hard but yeah
it's we've made a lot of progress on the
perceptions on how to undermine the
difficulty of the problem I think
everything about robotics is really
difficult of course you know the the
planning problem the control problem all
very difficult but I think what's what
makes it really you know yeah it might
be I mean you know
and I picked downtown San Francisco I
ate adapting to well now it's snowing
now is no longer snowing now it's
slippery in this way now so the dynamics
part could good I could imagine being
being still somewhat challenging but no
the thing that I think worries us and
our tuition is not good there is the
perceptual problem at the edge cases
sort of stout sauce and Francisco the
nice thing it's not actually it may not
be a good example because cuz you know
what - what you're getting for all
there's like because crazy construction
zones and all yeah but the thing is
you're travelling at slow speeds so it
doesn't feel dangerous to me what feels
dangerous is highway speeds when
everything is to us humans super clear
yeah I'm assuming light are here by the
way I think it's kind of irresponsible
to not use lighter that's just my
personal opinion
depending on your use case but I think
like you know if you if you have the
opportunity to use light are good your
injury makes more sense now so you don't
think vision I really just don't know
enough to say well vision alone what you
know what's like I there's a lot of how
many cameras do you have there's all
sorts of details I imagine their stuff
is really hard to actually see how do
you deal with would glare exactly what
you're saying stuff that people would
see that that that you don't I I think I
have more my intuition comes from
systems that can actually use lighter as
well yeah until we know for sure it's
make sense to be using lidar that's kind
of the safety focus but then deserve the
I also sympathize with the Elon Musk the
statement of lidar as a crutch it's it's
it's uh it's a fun notion to think that
the things that work today is a crutch
for the invention of the things that
will work tomorrow right they get it's
kind of true in the sense that if we you
know that we want to stick to the
conference and you see this in academic
and
settings all the time the things that
work force you to not explore outside
think outside the box I mean that
happened all of that the problem is in
safety critical systems you kind of want
to stick with a thing Sutekh work so
it's a it's an interesting and difficult
trade-off in the in the in the case of
real-world sort of safety critical
robotic systems but so your intuition is
just to clarify yes how I mean how hard
is this human element forger like how
hard is driving when this human element
is involved are we years decades away
from solving it but perhaps actually the
years and the the thing I'm asking it
doesn't matter what the timeline is but
do you think we're how many
breakthroughs away away from its in
solving the human robot interaction
problem to get this to get this right I
think it in a sense it really depends I
think that you know we were talking
about how well look it's really hard
because I'm just know people do is hard
and on top of that playing the game is
hard but I think we sort of have the
fundamental some of the fundamental
understanding for that and then you
already see that these systems are being
deployed in the real world you know even
even driverless because I think now a
few companies that don't have a driver
in the car yeah small areas he's got a
chance to I went to Phoenix and I and I
shot a video with lame-o and you need to
get that video out people didn't give me
slack but this incredible engineering
work being done there and it's one of
those other seminal moments for me in my
life to be able to it sounds silly but
to be able to drive without a with a
ride sorry without a driver in the seat
I mean I was an incredible robotics I
was driven by a robot and without being
able to take over without being able to
take the steering wheel that's a magical
that's a magical moment so in that
regard and those domains at least for
like way mo they're there they're
solving that human there's I mean there
were they're going fattening it felt
fast because you're like freaking out at
first I was this is my first experience
but it's going like the speed limit
right 30 40 whatever it is and there's
humans and it deals with them quite well
I detects them and a good negotiation
the intersections the left turns and all
that so at least in those domains it's
solving them the open question for me is
like how quickly can we expand you know
that's the you know outside of the
weather conditions all those kinds of
things how quickly can we expand to like
cities like San Francisco yeah and I
wouldn't say that it's just you know now
it's just pure engineering and it's
probably the I mean I know by the way
I'm speaking kind of very generally here
as hypothesizing but I I think that that
there are successes and yet no one is
everywhere out there so that seems to
suggest that things can be expanded and
can be scaled and we know how to do a
lot of things but they're still probably
you know new algorithms or modified
algorithms that that you still need to
put in there as you as you learn more
and more about new challenges that get
you get faced when how much is this
problem do you think