asking you to tell us where you were
born and where you grew up
yeah i was born in bratislava
which is slovakia
used to be czechoslovakia
and
what else um and you grew up there and
you went to
and i grew up there and i went to the
undergraduate school of electrical
engineering at
the slovak technical university i
graduated in 1950
[Music]
um seven and then
i worked in industry for five years
and then i joined the university
computing center which
bought the first computer in
slovakia from russia
the computer was a electronic cube
computer called ural
2 and i worked as
first as a maintenance engineer
and then
maybe three years later or so
i
became an assistant professor there
in the newly established computer
science department
in meantime i also worked on my phd
thesis which i defended
in 1967
and then i got an opportunity
to come to stanford and
i arrived to america in october 1967
and initially i was going to
stay only for one year but then
i got the opportunity to enroll as a
graduate student
and since my degree was in electrical
engineering i
really realized very quickly that i
didn't know what computer science was
so i enrolled as a phd student in
computer science and i got
my second phd in computer science in
1972 from stanford
and then i took a job
as a assistant professor
at university of pennsylvania in
philadelphia
in the newly established computer
science department
so what were your thesis on for your two
degrees
well in electrical engineering my thesis
was
on machine learning
i developed a model of
basically a feedback system uh
teacher student kind of learning
um probabilistic learning
and then at stanford my thesis was
on computer vision
in texture analysis it was first work on
texture
analysis who were your advisors
john mccarthy yeah and then um
in slovakia you don't have really
advisors you know you basically work
and then you have a committee and
how did you get the opportunity to come
to stanford how did that happen
um that's an interesting story
my boss professor gwozniack
in who was the chair of slovak
computer science department got the
opportunity to go to
england to look at the
the computers in in great britain
i believe he went to
well i forgot but he looked at the
hardware the new
architectures in england i'm not sure
whether it was in edinburgh
or no it was in manchester
that's where the first computer the
british
first computer was built
and he met there john mccarthy
and john suggested that
he would be interested to take somebody
from slovakia to study here
at stanford and so
when professor glostia came back
he told about this opportunity
and i jumped on it
and since i was the first woman who got
a phd
in electrical engineering in slovakia
that gave me a little edge
in the competition and so i was selected
to go
to stamford and what were
the differences so when you moved from
slovakia to
it was tremendously different yes
tremendously different first of all
you realize that in europe in general
the phd people
don't take courses they just
work on their independent research
and while here people
have to pass examinations and take
courses
so that was really a big shock to me
because uh also in the
during the undergraduate studies in
slovakia
the examinations were mostly oral
i mean in mathematics and physics you
had the combination
first solving some
written problems assignments and then
you went
for an oral examination in america
everything was written examination
and it was time constrained and i wasn't
used to that
so my first example is a failure
and but professor mccarthy was very kind
to me and
he supported me and and
so i got the second chance
which i finally passed and
and then my thesis my research was okay
so so that's how i made it
but the beginnings were very rough
because my english was not so good
i was not used to the examination system
and so it was and i was competing
with very highly
trained in mathematics in particular
highly trained people
and i also was a little older so i was
competing with 10 years younger cohorts
and they were much faster than i was so
it was rough
but i but i
i persevered
so never mind i i'm not going to take
the call
um whether differences between the kind
of programming languages that you were
using
well in slovakia i was coding
in hexadecimal system
i mean there was not even an assembly
language
in on this computer then i well here i
came
and we were using ibm
i don't know whether it was 360 or the
precursor
of ibm we used punch cards
and so every time you made an error you
had to create a game so it was a very
different
thing and the programming languages
that mccarthy was pushing was lisp
which was a very different language than
arithmetically based language like algol
or fortran
so so that i had to learn very quickly
the
recursion and you know
the lisp language
so it was very very hard
what kind of vision systems were you
using for the
texture and application well we had the
in those days there were no ccd cameras
so it was a regular
camera like yours and then
attached was a digitizer
which provided you digital images and
and then you worked offline on these
digital images
and the challenge there was that
first of all the memory in computers
were very small
we worked on small images so the
resolution was not very good
worked on 64 by 64 pixels
or at best 128 by 128 pixels but
so the resolution was rough and in
texture
you really need a higher resolution in
order to capture the local
properties so that was a challenge
technology just wasn't there everything
had to be
made in the lab
we had to build digitizers and
and deal with swapping the memory to
disk
all the time because the memory was
typically 64k or so you know
even i worked then later on i worked on
pdp 11 which had a 32k
memory the cache so
so it was very different time and
therefore the
the approaches you took were
quite different than today people use
millions of pictures and do statistical
analysis
very different do you think that's the
biggest change that you've seen in
the technology well the the
no no no there are many other changes i
mean
of course the the speed and the the
memory size of the computer
is is phenomenally different i mean
but the parallelism that the
the distributed computing is also
tremendously big difference and the
display systems
at that time we used
when i worked at stanford we didn't have
even a raster
display it was all just
vector displays that displayed
only the the contours
at the end of my stay at stanford
we got the first raster display which
allowed you to
display all the pixels but
in the beginning you know it was all
just displaying contours
were you working on any particular
application
no no that was one thing
that the lab had absolutely
freedom although it was supported by
the military in the darpa at that time
it was arpa i guess or darpa i don't
remember we
changed names but any case it was
we were given complete freedom to do
really
explore just the basic stuff
basic research was the key
yeah and so when you went to
u-pen after stanford yes
what kind of projects did you start
working on wow
that's a different story
when i moved to penn there was nothing
and so i had to build everything from
scratch
my first master's student
aaron snyder was a electrical engineer
and he and i built the digitizer because
we had to buy
these analog cameras a camera one camera
and then build a digitizer and then
i had i got some nsf initial grants
that allowed me to buy a raster
display so that we can visualize and we
the department had pdp-11s so
i was working on on that computer and as
i said
it had just a very small memory
and so we were swapping things back and
forth
i i started to work
on i basically wanted to continue my
my phd work and
one of the the first things that i
started to look at was texture gradient
and how can you how can you
interpret this three-dimensional
information from
the two-dimensional projection of the
texture gradient
so that was the first phd
with uh larry lee
lieber man right was my first phd
student
who then went to ibm research
and worked on had
had a group in robotics because i always
was interested in the three-dimensional
interpretation of the visual information
from the very beginning and
i always was interested in vision four
not just vision per se but
what is vision for i felt that it was a
good way of testing
your algorithms if you can
find you know the interpretation where
you are
or can you grasp things or
or move around i mean the the robotics
gives you
a very good testing
of your sensory processing
so that's what i focused on
and then i think
it was maybe 73 or 74
a man by name britain chance who just
passed away yesterday
uh 97 years old who was
a very prominent biophysicist
he invited me to look at
some of these
x-ray images from
red brains because they were really
interested in automating the
the image processing of these medical
images
so he brought me into this group
and i found it very interesting and so
that
started in my career in medical image
processing
um i worked with him or
for him for about six months but then
he was a very strong personality and my
joke was you
you worked for him or you didn't
and so i quit because
i didn't want to work for him
but then i continued with
with other people in the medical school
at penn
on medical image processing and
and i did some nice work there
what was the first robotic system that
you worked on
um well the first robotic system was
when i
had money to purchase puma robot
which was one of the first robots
available for the for academic
labs and
and around the
76 77 i started to
connect the manipulation
with the camera work
and grasping
was one of the and of course in those
days they had only two
two finger grasping so i
uh i initiated
um with the pen
mechanical engineering department to
design a three-fingered robot
and so we were very much ahead of time
in this regard um
around that time i also connected with a
french laboratory in toulouse which had
a um a rubber
sensitive pressure sensor
and i as part of the collaboration
they made me a a
what we called french finger you know
um ants which had sensors
so we were able to to do
to do this kind of you know tactile
connection and um
so i so my one of my first phd students
well he wasn't my first but i don't know
third or so yeah i think it was cert
or force peter allen who is a professor
now in columbia university
he did the first work of this
interaction of tactile and visual
information
and so that was
that who did you work with on designing
the three-fingered robot and
also okay it was oh with this
three-fingered hand this
was well
i forget the name of the professor
in the mechanical engineering department
he passed away to the
i kind of outlive all all my
collaborators
um but um
i think the student who designed it was
abraham's son
if i remember three-fingered
hand with a palm and then we put some
tactile pressure sensors on on on that
so that allow us to
to connect you know find where you are
connecting
and actually um
ken goldberg who is here a professor
did his master degree with me on
on using these tactile sensors
for recognition purposes
so i also during that time connected
with psychologists
who were prominent in this tactile
perception
one of them was susan lederman
who was a pro who who is an outstanding
professor scientist in
in tactile perception at queen's
university in canada
and then she connected me with another
professor
roberta klatsky who is a professor
at cmu in psychology department
and we had a very fruitful collaboration
uh you know with i learned quite a bit
from these
two people on
how people perceive that for example
people have certain procedures
motor motoric procedures when they want
to find
how hard something is or what is the
surface texture
and so there are these modules that
people
procedures that people use for
exploring they called it exploratory
procedures
yeah and so did you
end up applying similar procedures
absolutely i absolutely implemented them
yes were there any particular
challenges in that well the challenge
was that
the human skin is it has a very nice and
high resolution
and my tactile sensors were
basically on and off you know were not
very
very highly resolute so we had to
supplement it by
by multiple measurements and
so yeah i mean to really
and and of course
you could really only observe behaviors
you couldn't go into you know what went
on
in your brain so we were inferring
more you know the the matching of the
computational
models with the psychological
on the behavioral level that's all what
we were able to do but
i i was really impressed by
how carefully these psychologists
do their experiments
because you know people have so many
capabilities
and how when you want to measure one out
of
100 different possibilities how you have
to
uh isolate you know everything else
in order to measure the one that you
want to
measure so it was really a
educational collaboration we had a joint
on navy grant that allowed
us to do that then unfortunately
we couldn't continue well
our funding expired and
we didn't continue but i'm still in
touch with them i mean
yeah were there other psychological
theories of
vision and perception that were
important to your work yes
j.j gibson absolutely jj gibson
was my hero because
he really had his ecological
affordances approach to
psychology and that very much fitted
with my way of looking at the world
so he inspired me by
active perception
where he he recognized this
we don't only see but we look
and we don't touch only but we
feel that there were some control
strategies
and that fit me extremely well
because from czechoslovakia i had my
control engineering background so
i was able to connect this control
approach with the
signal processing approach
that was part of the
sensory perception
of your other publications were in
kybernetica was
cybernetics that's correct yeah yeah
yeah yeah yeah that was
yeah so what was cybernetics like and
like well it's it's an interesting
question because because
as you know cybernetics was started
actually um
before the second world war by people
like ashby
you know who were
and then later on wiener you know
and these people were in vienna
before the second world war and they
were very much
interested in using the
analog feedback system theory control
theory
for modeling some of the uh
psychological behavior you know
ashby built this first mice
mouse kind of running around
the the maze and um
that influenced me quite a bit
you know coming from this control theory
background
to interested in i always was interested
in these
biological systems how they work
and so that connection was very natural
to me
from czechoslovakia and
and of course as you know the russians
have a long history in
control theory pantry again and
and these people in moscow were very
prominent
in the feedback system okay
the ana the continuous feedback system
okay so then when i came to america
um john mccarthy and marvin minsky
uh who were originally students at
princeton
and studied under a church
who was
big on the logic okay they were
basically logicians
plus they were also influenced by the
digital the switching theory that
did and the digital technology was
coming slowly around
and so john
had this really believe
that the what is really important
is to think about representations
uh as opposed to this feedback system
he really was interested in what what
do we store from this from our
experiences and
and he also believed that what we store
is not a continuous signal
but some kind of a a
an extraction of symbolic representation
and so when i came to the to work
under him and in the in the artificial
intelligence laboratory
um the emphasis was in this
symbolic representation
actually i don't know whether you know
this but in 64 or 60s
or maybe 60
or maybe even before there was a famous
meeting
that he and shannon organized
where they put up first this hypothesis
on representation
because as you know shannon was
interested in the
information that the channel's theory
and then he went to
washington and and wanted to convince
the government that to support research
and he told me personally that
that he introduced the word artificial
intelligence in contrast
to cybernetics as a
as an argument that what he was
proposing was very different
than what the cyberneticians were doing
now you know there was a parallel of
cybernetics here
by the sky rosenblatt of neural networks
they all were intrigued by
what we carry in our head and
how and how to use the current know-how
of technology or mathematics and
technology
to model what's happening in our brain
and you know that's what so
so john and and mecca
and minsky and then later on simon and
yugo
they really had heard through this
hypothesis
that is more important the
representation as opposed to the
feedback and that this representation is
symbolic
um so that's
what while the eastern europeans
including the russians were adhering
more to the to the cybernetics
the the analog feedback now
remember that in this country also there
was the control theory
group that was still pushing
the feedback systems and
estimations of
systems and so forth so but
mccarthy made it very clear that
he didn't want to do that so
he really wanted to be different and
initially you know people were very
excited
that this is it that this is really is
going to revolutionize
the the understanding of intelligence
i think we came a long way and
i think now there is a much more effort
to
to combine this um
analog and or continuous systems with
the digital systems
under the name of hybrid systems
you know and as you know
mother nature doesn't care
how we call it and i mean
the mathematics is really an abstraction
it's a model
okay mother nature does its own things
including the biological mother nature
and
we still don't know what we carry here
right
and but in meantime
as engineers we want to build some
useful systems
and to me it's rather clear
that that some
some extraction is is useful
but you have some kind of a hierarchy
you know where you have the initial
signal
and then you compress and compress and
compress and clearly symbolic
information the words
is the most compact representation that
you can think of
right but it's also ambiguous
unless you put underneath a lot of the
semantic interpretation and the
contextual interpretation
because the same word can have different
interpretations my classical example i
like to use
is what is large what is small
is very relevant right relative not
relevant relative
yeah and and so forth and so on so it's
so you can have
you know many interpretation depending
on
the context
so were you at any of these meetings
that you talked about with mccarthy
oh yeah oh yeah yeah yeah yeah he was a
good friend
i mean i i really cherish
our relationship and it's very
unfortunately is not so well right now
but
but i have a lot of affection for
him he really facilitated
enabled who i am absolutely
and i will be indebted for that forever
um so what kind of machine learning
algorithms were using
before you came to the state oh okay
it was a feedback system and it was
modeled
after some psychologists
bush and muscular wrote a very
famous book on the subject
and it was a
basically a first order different
difference equation
put into a matrix formulation
of transitional probabilities
and had coefficients of how fast can you
learn
and what the saturation is and
that was motivated by these
psychologists
and i just put it into this mathematical
formulation
how did you come into contact with all
the workplaces
of our earth psychologists very early on
books books
just books yeah
but how did you decide to actually go to
psychology
well i was really always very intrigued
by
well first of all i always loved
mathematics
and uh and in a different world i would
have been a mathematician probably
but in under the communist regime
i looked for engineering which had the
most mathematics
because the mathematician
mathematics was was very
colored by by the by the
communist philosophy so
and they needed engineers so they were
more tolerant
you know with your with your
philosophical views
uh so i felt more
than my future might be better served if
i am
if i'm trained as an electrical engineer
but i was always interested in uh
you know how people behave how people
do things and so it's it's it's very
intriguing
and it's still intriguing i mean
after all i mean look what kind of
robots are we building right
i mean i have a colleague here peter a
bill who is
trying to have a robot to fold shirts
you know and stuff like that i mean
you know we are aiming more and more to
sophisticated
activities mimicking people actually
this was the original
ambition of john mccarthy as well and it
and it's fascinating how it proceeded
that for example
when i was there he was pushing very
hard for these
chess programs okay
and it turns out that
actually in these games
we were doing much better
doing computer chess games than
grasping or moving around and stuff like
that
and what it tells you is that
you know working with mental
decision making
if we understand it better and therefore
we can program it better
and if you have a lot of compute power
you can do a lot of
searches and and and therefore you can
do better than in this
interaction with the physical world that
part
is still not
well understood the flexibility that the
biological systems have
is phenomenal and of course it's a
result of
millions of years of evolution but
nevertheless
i mean if you look at biology as i
do in my spare time it's phenomenal
that how different creatures have
developed their
sensory and motor apparatus adjusted to
their
environment and survival
i give you just one example there is
shrimp
which has in
has 12 different color filters
in order to discriminate between their
enemies
and their friends through different
color combinations we have only three
okay and so so you you see this
that the the biological system
adaptivity
over long time
uh has adapted to survival
to this environment to me that is
fascinating
that is fascinating
and and very inspirational
and has that played into your work as
well very much so
very much so very much so when i want to
understand
you know how much sensor
you need to put on robots
what do you want to extract it's very
clear to me
that it's very environment and task
dependent
it's a basically an optimization game at
that biology place
would you call any of your projects in
particular biologically inspired
oh yes oh yes most of them
yeah now
i submit to you that
frequently i had to
reduce some of my activities to
be pragmatic you know because of
limitations of the hardware
or the limitations of know-how
you know uh of the current
scientific state of the art
i mean let's face it
some of the tools mathematical tools
that we use today but not available
25 years ago or so and i'm not a
mathematician
i wanted to be one but i'm not so
i i just i'm a consumer of mathematics
could you give us some examples of
particular projects
that you did that were inspired by these
ones
one of them is this combining the
tactile
information with the visual information
i very quickly realized that the vision
which is an indirect information
doesn't give you enough information you
can
the eyes can be fooled so you need
the contact in order to really
measure the the weight for example you
can't
get weight from vision
the mass
so and so this is still things that you
were doing at
u-pin yes okay when i came here
i changed as one
shoot obviously um
i started to focus on
observing people and so
all my activities right now are
centered how can we use technology
for improving communication
between people that's why i started the
tele-immersive
laboratory and and
i'm also putting
sensors on people so that i can
for elderly to monitor people so that's
what i'm not focusing
but i people are
a very nice mechanical system
i mean mechanical dynamical systems
you know and it's a lot of fun to
to and being an experimentalist i
basically uh build labs
facilities so that we can
get as best observations as we can
and then based on these observations i
use
models mathematical models that
are out there or that my students who
know more maths
that i do bring to bear
to explain the data so i'm acting more
as a experimental physicist if you wish
you know in physics result is not a
result
until you're verified by some experiment
and the same in chemical chemistry
and i have that philosophy about
my work too that
result is not the result until i verify
it
with data sometimes i go the other way i
take observations and then
i try to fit models that
explain the data but sometimes
we have you know i'm surrounded by all
these
theoreticians around me or uh
who have all kinds of models and i say
well
how can i validate this model
so that's what i'm focusing since i came
to berkeley
what was the first robot system that you
built at berkeley
at berkeley i didn't build the robot
system now
but the television the tele-immersive
laboratory it's
48 cameras surrounded
and um the big
thing that i struggled with can i drink
a little coffee
i mean even with the television
the big thing that i struggled in that
lab
was real time
capturing and reconstructing
the stereo reconstruction in real time
so we started with four frames per
second
then we graduated to seven frames per
second
now i'm happy to say it's weird 25
frames per second
so big big accomplishment and it wasn't
just by hardware
it was also clever thinking about how
you use
the raw data and i was blessed by
some very good students who
helped and did basically all the work
so i'm just a conductor
can you tell us the names of some of
your students yes with pleasure
so in i started with
um hong sang who was a postdoc of mine
uh he came from penn and
he built the first version
of this and then the real
breakthrough we got when i started to
work with
edgar lobaton l-o-b-a-t-o-n
and ram vasudevan
who is still with me here and they
designed
a new reconstruction algorithm that
sped up you know the whole
reconstruction
and um
let's see and then gregory kurilov from
leblanc came as a postdoc and now he's
my
senior engineer who is
basically keeping up the lab improving
doing a lot of the
applications you know one thing is
that these kind of facilities need money
so i always joke that basically the
applications
are selling and um
and then we do the science on the side
what kind of applications do you have
okay okay so
the first application we did
with the dancers and we still continue
um because they were very
receptive to the weaknesses of the
technology so for example in these four
frames they had to
move very slowly but
but i also began simultaneously
uh collaboration with professor nashted
in illinois who happens to be my
daughter but
yeah you did i see so you know okay yes
and so she built
a similar lab smaller but similar lab
and so we had a joint grant
which enabled us to have a dancer in her
lab
and dancer in my lab and show that they
can dance in one
virtual space so that was the first
application
um then we
then we moved from there on
um to so we have currently
application with archaeologists
who go out and they digitize all kinds
of
artifacts you know caves
and sculptures and the collaborator is
interested in the collaborator is
interested
to connect with different
archaeologists so that they can
be put into the same environment
and and then come up with a common
interpretation
because apparently they cannot agree on
the common interpretation so that's one
application
um now we have a
collaboration with an aerospace company
eads
which is um you know
international company and they are
interested to use this technology
for discussing design
distributively and this technology is
ideal for
that we have a application
for um
medical application physical therapy
okay where remote coaches can
guide people in this
environment to they can get into the
same environment and tell them
which way they should be moving and so
forth
and um i'm hoping to
to have more of these applications but
right now
you know with all these applications
the biggest challenge is to find the
right collaborators
because i found that especially
in the medical area but in any other
area
as well people are quite conservative
and they don't have very much
imagination
what this technology can do so unless
you show it to them
look you can come and do this manipulate
move around you know change this change
that
in real time they just cannot comprehend
you know they cannot visualize what this
can do
so i'm spending a lot of time
cultivating these
collaborations which is not trivial but
but little by little and and the good
news is
that as you can see i have been
doing this for at least five years
now and the good news is that the
components are coming
so you can buy now commercially
a stereo camera which will give you you
and you can buy
now 3d tvs are coming on on the market
so
so the 3d you know i have been accused
often why do you need 3d
why not just have video conferencing
okay well if you have just video
conferencing you cannot bring
two people into the spatial
configuration
you can have next to each other but not
in the same
space that's the difference so
so i had to struggle with all this
resistance
for 3d the other thing is where you need
3d is think about some of these games
like basketball or
football or soccer and if you want to
look at from
any angle and once you have it in the
computer
the 3d then you can using just simple
graphics rotate
and look at the place from any angle
you wish to so that's another place
but of course this technology is not
cheap
and so that's another hindrance before
you can really make it
big and widely available and
look what happened to robotics for all
these years
you know i remember 25 30 years ago we
all were hoping that
companies industry will be all
roboticized
well it didn't turn out that way it
turned out that
you know the the develop the
value that we were selling about robots
which was
reconfigurability reprogrammability
was just not there and so it was cheaper
for the automotive industry and all
these big companies
to make a fixed automation
okay well now i think we are slowly
getting there
yeah but you know
it's still you know with this
tele-immersive technology we are
learning
that we still it's not a turnkey system
you know where you can you can just
plop it down and leave the room and let
people use it
not just like a television you just
click you know it's not yet there
especially with the cameras because it's
sensitive to the illumination
sensitive to the colors in your
environment
so ain't that easy so it will take
couple of more years before we get there
to the components are coming which is
good news
and i'm hoping that i can show the value
of it
uh in the house area you know
i'm hoping but you know
the result
the proof to the pudding is to cook it
and eat it
who are some of the other people you've
collaborated with over the years
ah good question well um
let's see many i have many collaborators
um on this tele immersion i really
worked very closely and got very
motivated
by henry fuchs from north carolina
we are not working now together but
you know i i wish we could find some
place where we can work because he's a
very bright guy
and i really enjoy working with him um
i still work with some of my colleagues
at penn
you know and um costas daniel ladies
vijay kumar
george papas you know um
here at berkeley i collaborate with
my colleague next door claire tomlin
shankar shastri so
those are my closest collaborators
um
so who else i'm collaborating right now
um oh yeah yeah i collaborate with
medical people okay so
um i collaborated
uc davis with dr j
han who is interested in monitoring
these people who have neuromuscular
diseases
and this technology is very good for
that and i'm hoping
that we can develop a kind of ambulatory
system that people can use at their
homes they don't have to come
all the time to be to the clinic and so
on
uh but again i tell you the
interfaces the the human interfaces
is really a challenge how to make it
so the mom or dad can use it
easily with children or with elderly
people
i'm actually focusing on these two
categories on children and elderly
because they are the most vulnerable
people um so that said uc davis
here i'm collaborating with dr edmund
settle who is a
a research professor at this
school of public health
and i'm of course have been
collaborating for years with professor
lisa weimore who is a professor
of dance here at berkeley
and as i mentioned i collaborated with
clara
who else do i collaborate well that's
about it
well the archaeology is professor
maurizio forte
from uc merced
and how do you find yourself with these
such cross-disciplinary
collaborations do they find you do you
find them
you know how do you how do you for
example start working on something like
archaeology
how did we started this archaeology
work okay all right
i know okay well as you may know
i came to berkeley as a director as a
founding director of citrus
and the task of this
institute was to
see what can information technology do
for societal
needs and
so so i had basically
put to myself as a task but
one of the tasks was to cultivate the
relationship with
companies because the original money was
so bad we can
do more what they call a useful research
for industry and we were measured by
number of startups and that kind of
thing
but i also felt that there was another
community that served as a society
and that was the social science and
humanities
on the campus that i found didn't take
advantage
the information technology to full
extent
so i started to look around on berkeley
campus
and try to see
where information technology can play a
role
and as i was digging around
that's how i met lisa weimer i gave a
presentation
in the school of humanities look this is
what technology can do
and so but as i was digging around
i also found out that there was a group
here led by this
and one thing when you are in a position
like director of citrus i had little
money so i could
sort of give out money to seed
some projects so i connected with
professor lou lancaster
who is an expert on buddhism
and he was very much interested in
taking these old maps of the silk road
and digitizing them and show
how the silk road evolved you know
because they were
stationaries of these buddhist
monasteries
and i thought that was fascinating in
application of information technology
and through him
i started to think about
more where information technology can
play a role
and archaeology was a no-brainer
i mean when you think about it
archaeologists destroy
their place of inquiry as they are
digging
so i said it makes sense
to as they are digging take images
and use our technology to reconstruct
once they are done so that's how i
connect it with the arc
unfortunately again you need the right
collaborators so
at berkeley i approached the
archaeologist and they said
just give us the money and leave us
alone and i said
that's not how i think of collaboration
so but so i found this so
in one of these meetings that we had
i.t for humanities
i met this professor forte and then he
took a job at merced so
we started to work together how did you
get involved with citrus to begin with
oh god
well before i came to berkeley i was
for three years in washington at the
national science foundation
and i was director of the computer
science
division and concurrently
shankar shastri was at darpa
heading the the information technology
directorate and so
we would meet in these you know the
washington has these inter agency
meetings
and we would meet there and we became
friends
and then he left before i left
nsf he he was called back to be a chair
of
the electrical engineering computer
science department
and at one point he said well arjuna
they are looking for a
director would you be interested
and i said sure that sounds very
interesting
so so i applied and then i was selected
and what other people are you working
with within citrus
um well of course
as a former director i am kind of
involved in indirectly
the current director is paul wright so i
talk to him from time to time
i don't know directly you see
in citrus citrus is an organization that
facilitates
connections so i don't know what
how to say how to answer your question
um that there is a person who
is kind of a manager of this
health related project
so i interact with him
because of the uc davis connection
but other than that you know
i mean they are mostly uh
they organize meetings and so so i
there is no real remember i'm
more interested in the substantive
interaction
and you know these are administrators so
i don't have very much to do with them
and so in a sense after you were you
finished being the director
it did help you have some of these other
um
collaborative relationships yes yes yes
yes yes yes yes
so when you were at penn what were some
of the other robotics projects you
worked on
oh god many
you know you you you you have students
so you have projects right
boy well perhaps i should mention
um well
i don't know it was always about this
interaction between manipulation
and envisioned and then i had
worked with jana kochetska who was my
phd student
who is now professor at
george mason university she worked on
mobile we had little mobile robots and
vision and and kind of co
that her job was to model the
the cooperation between two robots and
when they move together like a flock of
birds or so how they
coordinate and what they have to tell
each other and
and how independent they can be
to you know it's like the scouts meet me
at place x and stuff like that so that
was um
i don't know what to tell you i mean
there were
multiple projects um
i had a very outstanding student
actually my
perhaps the most successful academically
student
stefan mullett who is a professor at
lake or polytechnic
in france and he
his claim to fame is he introduced the
wavelet
transformation into the
image processing he had a very good
mathematical
background so he was able to do some
interesting stuff with in computer
vision
and we are still in good
contact and is going to come and visit
me
again and he's doing some very
interesting work again
so so that was stefan
um
anyway there are many many but one thing
i should really mention
that i had these two parallel lives
in the medical image processing i
invented the
anatomy atlas the brain anatomy atlas
and that was with a israeli student of
mine
jaim breut the roit
who developed a kind of
deformable matching process
that you take from because
you know one brain is similar to another
brain but not exactly so it
so he modeled it as a membrane and
developed a matching process to match
and that really picked up and now
everybody is using it in the this
mri ct positron emission tomography
dyspo since 79
80's yeah
was there anything that you would have
really liked to pursue in terms of
research but didn't get the chance to
i don't think so i don't think so i will
say that
pen was good for me
for the as you know pen engineering
school
at least when i was there was not very
highly ranked
and so we did have a difficulty to get
good students and all that but
i must say that pen was good to me
in the sense that they didn't interfere
with me so they let me do whatever
i want chose to do
and as long as i was able to raise money
and build lab and everything else
they um they didn't
didn't interfere they
and so in that sense they were very good
both colleagues as well as the dean who
was at that time joe bordonia
who was very supportive of me and i am
most grateful to him for that
and as as the lab grew
the grass lab uh and the reputation grew
i was getting better and better students
as you can imagine
and i must say that i'm very proud
what what the grass club has
accomplished
and after um
job ordonia left i think this um
well was it joe badonia now there was
another team in between who didn't do so
much but
the current dean eduardo glenn
has done really a very fine job
in terms of raising money they built
new three buildings uh they hired some
very good people now of course
all the very good people were around the
grass club
so you know how it is i mean
good people attract good people but the
other thing that happened at penn was
that
some of the old guard in the department
retired
so they were they had the opportunity to
hire
and they really hired well so
i will say that the pen engineering
school
is on its way up and that gives me a lot
of pleasure
because it really germinated
from the grass lab which was from the
very beginning
a very interdisciplinary laboratory i
designed it that way and
so it benefited the whole school
we didn't have chemical engineering but
we certainly had mechanical
and electrical and computer people there
from the very beginning and
as i said now they are doing very well
and you know
my husband complains that i left and so
forth and so on but
i think i did the best thing for the
the place that i left
because you know
i looked at other labs and
if i would have stayed it would have
been
all the time the baichis lab and i
didn't want
that happen i really wanted the
next generation to really feel
ownership of the lab and do their own
thing and put their mark and i will tell
you
after i left vijay kumar took over and
he did a
phenomenal job i mean he is
is fantastic and
he's a good scientist and he's a human
on the human level and and so and
he hired george papas and
actually i hired costas when i was still
there
but you know they hired the younger
people
i don't want to even name many of them
but they they just hired
outstanding faculty and that's what
makes the place
work so vj did a phenomenal job
and after vijay i think posit george
took over and now i think it's kostas
but really the grass club
is really basically helping eduardo
in the leadership i mean i think
vijay is the undergraduate dean and
george is the graduate dean i mean what
can i tell you
it's all incestuous but for good
good goals and purposes
so i'm very proud of that
you know when you look back
at my age and you say well you know
people talk about legacy right
i never think of legacy but i mean but
you look back and you look at your own
children and you look at
i mean this is these are my children you
know
and i am very proud of him
what are some of the challenges for you
in the future for the future for the
future
i don't think you want to record that
my husband is one of the challenges
well okay
i don't know how many more years i have
but
anyway i'm still in good health and my
brains are working so
it's okay
here is what i would like to see
happening
and with my you know little angle
first of all i like to see this
technology really be useful to people
i like to
[Music]
i i'm hoping that before i die that i
will see this
tele-immersive communication work
because in this globalized world
if somehow we can bring people together
more
then perhaps we can avoid
all these wars and misunderstandings and
all that
to me communication is very important
you know
and yes we have social networks and
email and all that but you know
especially when you talk about foreign
cultures
i am convinced that we communicate not
only with verbally
but also with our body and that was my
idea behind the televersion
that you want to see and interact
as a whole person not just verbally
there is a lot of communication going on
in terms of how we move
so
and all the modeling i'm doing is
in better understanding these movements
so think about
it and this is really almost no
practical application but
if i can model
different cultural movements
i can have a quantitative
comparison of cultures
take greek dances versus
african dances versus eastern european
dances
there are certain messages there
if i can somehow mathematically capture
that perhaps that could
could contribute some better
understanding
okay i'm look i'm an engineering
scientist so that's all what i do
but if that can contribute
i would be very gratified so
this this this using technology for
better communication and that relates
also
to facilitate people to live alone more
comfortably so that
you know they don't worry about
i'm a very independent person i don't
want to live
if i cannot be independent and
i think many people are like me so if
technology can help to that
and the pieces are there the system is
not yet there
you know but the pieces are out there
uh i think we will make a revolution
in this regard now the lastly i
can tell you i'm right now writing
this proposal that's what the microphone
did oh i'm sorry that's okay
that was very animated right right right
right
right body language right right body
language speaking of body language right
so the proposal i'm writing right now
and starting a collaboration
with people from oregon health center
and in nutshell
what is excites me is that there is
again psychological
and behavioral evidence
that your mental health is tightly
connected
to your physical health
and vice versa so it's a standard thing
when you
you know in my age i have all these
friends who are retired
and and if they don't move
if they don't exercise they get mentally
also depressed
so what we hope to
do is study this connection
and there are way how you can observe
the mental
through various games and so and i also
have technologies to
study the physical so if
you can understand this formally
this connection then
one can advise people look
this is what's happening in numbers
again i'm after quantitative
understanding of this connection
which then can get feedback to people
to say you know you don't need to take
pills just get off your butt and do
something
you know and you will feel much better
here and
why you can ask why this has
been done i will tell you why
[Music]
because you have two different
communities
the cognitive part is studied by ai
the artificial intelligencia okay
and the physical part is studied by the
control people
and hardly they they hardly ever talk to
each other
they use different mathematical tools
and um and there are two different
communities so i
somehow like to bridge it so that's what
i would like to see
before i kick the bucket
and in terms of just robotics in general
what do you think are some of the
like where should it be going or where
oh boy that's a loaded question or where
would you like to see it going not where
it should it's kind of a
okay um
well there are many different levels
okay
so you have robotics
i really i'm really concerned about
the the young people in
who have no jobs and
and i'm not sure robotics will help them
you know because these people for
whatever reason
they are not able to
go beyond maybe high school
whether it's it's irrelevant why but
they are not so one thing that
i've been thinking but i don't i don't
have
the means or energy to do that that it
would be nice
if we could use this technology for
training then
traits you know how to be in
manual electrician or how to be a
masonry or how to be
a you know whatever
trade you know with your hands and again
because i work with this
three-dimensional world
this technology should be able to help
them
that even you know in this virtual
environment they could
try to play with some of these physical
objects
but i'm not sure you know again it's a
question of money and so forth
okay now um
so in in manufacturing i think robotics
is going to gain credit namely
people are making efforts making these
robots more reconfigurable and easy to
program
and easy to use and i think that's
coming
but i think more interestingly
uh the automobile industry is getting
much more about the size
you know and i'm
involved in in
you know how to observe people when they
are distracted
with their cell phones or with whatever
else
you know and give them a feedback either
you know a sound or something don't do
this
you know or or in fact
slowing you down and stuff like that so
the automobile industries is starting to
use a lot of our technologies
you know either you know alarming you
you are too close to somebody
or parallel parking and stuff like that
i mean that
that's already the industries
is starting to use it so i see a lot of
applications there also monitoring
traffic
you know so that's all in transportation
um in domestic
robots
it's not clear to me and i tell you why
bunny safety
it has to be simple that's why this i
robot made it
because it's 200 or 150 or so
and it's just very dumb you know just
doing vacuum cleaning
so you know people talk about toilet
cleaners and
you know kitchen and all that
the variety of environments is so huge
that it's not obvious to me how soon
that is going to be
happening on the other hand
i i already see some reasonable
applications
in these institutions like
hospitals delivering you know
meals and stuff like that um
so you know airlines probably will be
using more robotics
it's all a function of
easy to use robustness
and simplicity
and as you know
30 years ago when i started 40 years ago
we were dreaming about these ru r robots
that will do everything everything
i don't think that's going to happen i
think we will have specialized robots
or robotic technologies
yeah rather than
you know a universal
robot that will do right
when did you first encounter the are you
are
wrestling or something else oh boy
i remember it was by carol chapek
and that was just before the second
world war
and my parents had his books
so i think it was in the 40s or so
early yeah do you think that influenced
your interest in robots
i'm not sure i'm not sure i cannot
remember
that far was that that was maybe your
first
kind of being aware of something called
a robot or
or no yeah i mean it was a book
that kind of my parents had read it
he had another book about dogs i
remember
yeah pictures of dogs yeah he loved dogs
or maybe that was his brother there were
two brothers
chapping brothers and
the tragedy luckily one of them died
before hitler came to czechoslovakia
because they were very anti-nazis
and the other one i think he died in the
concentration camp
bad time yeah
i think we have exhausted the very thing
that we plan to ask um