and so if you could just start by
telling us your name and
where you were born and grew up and went
to school okay
so my name is radhika nakpal and i grew
up in
amritsar in india and i was actually
born in the u.s my father did his phd at
georgia tech
and but when i was eight our whole
family moved back to india and i didn't
come back to the us
until undergraduate which i did at mit
and how did you decide to go to mit what
did you study there
well um at the time amritsar had a lot
of turmoil
and there was a lot of terrorist
activity and the the city was sort of
shut down we used to have curfew at
seven
so i think as a high school student my
goal was to get as far away
from home as i could and i applied
to a bunch of schools in the u.s
figuring that was far
and figuring that i could potentially go
there
but i didn't know a whole lot about any
of the schools and there wasn't the
internet so
we would mail and they would send us
little brochures and the brochures was
basically all i knew
and my father knew a bunch about the
university so i applied to five or six
universities
and i decided to go to mit for for a
couple of reasons one of which was that
of course mit was really well known for
being a great engineering school
but also they had this was 1989.
they had a great program where they paid
for your tuition if you couldn't pay for
yourself so as long as you were admitted
they figured out how to cover you
so my parents were pretty poor at that
time so it was a really big deal that
they could do that and other
universities couldn't and so i got to go
to college
thanks to that program and what did you
major in
i didn't know what i was going to major
in actually so i
went in thinking i would do electrical
engineering but mostly i knew that i
didn't want to do biology
and then i didn't want to do mechanical
engineering and i didn't know
anything else but my first computer
science class 6001
that was it and then i think 6004 which
is the
computer architecture class so
programming and making computers
and i was hooked and how did you
wind up getting interested in robotics
that's a pretty long
path so i was initially interested in
computer architecture and from that
i got interested in networking and
distributed systems and parallel
computing
and i got a chance to work at bell labs
and that was really fun that's actually
where i got interested in research that
was the really big
eye opener for me for what it meant to
do research how people had fun doing
research
and so i told them i was like i want
this job and they said go get a phd
but when i joined for a phd i i sort of
floundered a little bit i wasn't sure
what i wanted to do a lot of topics that
i thought i understood
were really different from what i had
predicted and there was a new project
that
just started at that time called
amorphous computing was jerry sussman
tom knight and hal abelson
and the idea was that we would learn
from biology
as a way to think about programming
systems that had huge number of parts if
you think about a distributed system
like a network
they're saying you know cells are a
network or ants are a network or physics
is a network of molecules
and if you could compute with large
numbers you would think really different
about compute computation and it was
just such a
they've published this white paper it
was so exciting so
a bunch of people flocked to that group
leaving other groups
and actually the the area called
synthetic biology
grew out of that as well so it's like
not only would we learn
from biology we would program cells the
way we program computers that was kind
of the idea
so it was a really really exciting time
and
i i think that in the beginning we
thought you know what would be a
physical
instantiation of this idea so putting um
sensor networks was one example uh
modular robots that are kind of like
made of different modules another
example
and then people were talking about like
smart dust and so it was a super
exciting time a lot of different fields
were
sort of pushing in this area of making
really cheap individuals
but even then i didn't want to do
robotics and one of the reasons was it
was really hard
and it was really expensive to own a
robot
computation was really hard cameras were
really poor
so i by the time i graduated it was
still
super hard to do robotics because all of
the
processing and information and sensing
was very different but
as i started as a faculty maybe three
four years down
3d printers started coming online
just so many different ways you could
actually build robots you could put kits
together
and the cost went down and then i
thought oh i don't write a grant
because i thought oh you know if i
wanted 10 robots that would cost me like
a huge amount of money as soon as that
cost went down
i got excited about owning robots and so
i was maybe my third fourth year
yeah about in faculty when i really
started doing robotics not just
talking about um not just doing the
theoretical side but actually owning
robots and building robots and now we
have a thousand robots in my laps
so it was a very quick trajectory from
you know nothing to a thousand
so uh what what year were you at bell
labs
i was at bell labs in 19
this might be tougher you might have to
verify this uh 92
90 no 93 and 94. so i first went there
mit had a program where you could go
you could partner with a company and you
would do your masters with the company
so you'd go and spend a whole semester
there
as part of your five year program and so
i spent a semester there and then i
spent a winter there
and then i deferred grad school and
spent a year there because it was a lot
of fun
so i worked most closely with ray
mcclellan mclellan
who's a who works in computer
architecture
but i was actually right next to the
unix room so
i got to meet brian kernighan and dennis
ritchie
and ken thompson and dave prusado and so
it was this whole group
in computer science that especially at
that point
c was the only language we used and
bjr and stross troop and c plus plus was
sort of starting to happen
and so it was just this illustrious
group and they would all go to lunch
together and i could go to lunch with
them and they'd talk about their hobbies
and
they'd talk about their passions and
then you know we'd go and we'd work on
walls and it was great because
bell labs everybody there were not that
many women and there were not that many
students
and i got treated like royalty so it was
really exciting to have all these super
famous people
who still know me and still check up on
me every so often to ask how i'm doing
so it was really um it was a really for
me a major experience to be at bell labs
and uh for your phd what was your thesis
topic and who was your advisor
so my thesis talked was with jerry
sussman
and hal abelson and my thesis topic
was really about how you could take
an idea of what you want a collective to
do i mean this is actually maybe
you know was the my first example of
being able to do this and since then a
lot of my research is built around this
idea
that if you have a collective of
individuals and they all have simple
local rules
what you can't do is design the rules
bottom up
because you're just going to be stuck
you know
trying to see every variation of what
goes on and
you know if you look what an individual
is doing it's not well connected with
the
global one so is there a way to go
inverse is it possible to write like a
compiler where i say well what i want
the group to achieve is this
and then the computer sort of figures
out what it is
that all of the individuals should do
but that program has to somehow deal
with the fact that
some people may not you know some of
your robots may not work or some of them
might get lost or
you don't know exactly if there were a
thousand or a thousand
two hundred right so at that numbers you
don't want to be counting anymore
so traditional planning thinks about
maybe smaller groups that as an
individual what can each of us do and
how will we coordinate together
in r1 we're thinking you have a bunch of
identical individuals and they're
not able to coordinate well but you
still want to achieve something so you
can
up the number but you can't make them
more predictable
so in my thesis i actually was thinking
about a programmable material that might
have been made out of many different
actuators
that would fold and what's interesting
is there are now folding robots
you know so for me that was an idea and
a way of thinking about
a new sort of active material or active
environments this was a lot of
computation would be embedded in
everything that was kind of the idea and
if it's embedded in everything then it's
programming it means you can't be
rebooting
individual bits of everything it has to
be sort of self-managing in some way
so i was thinking about active
structures but most of i think the real
gist of what i did was show that you
could take very complicated ideas
globally
and systematically compile them
and who else did you work with or
interact with while you were a graduate
student now
so um i think it was a pretty
active time so um obviously there was
all of the synthetic biology stuff going
on so
tom knight was sort of one of the the
igem started around that time which is
these huge competitions where people
come and program sell so i was
part of like the first set we were maybe
20 of us
and now the competition has like 800 900
people
so it's huge it was a huge growth um
but the other group is of course the ai
lab and so i was always connected with
people
in robotics so halenko actually all of
rod brooks's group
just because what i was interested in is
very
what i'm interested in now is very
closely related to embodied intelligence
the idea that complexity that you see
isn't necessarily arising from complex
decisions
and complex thinking at the level of the
robot it's arising
from the interactions with the
environment or the interactions with
others
and that that's that that's what you
want you don't want to make a more and
more complicated robot you want to make
a simpler and simpler robot
so his group had a lot and in fact i
read i remember
for one of my they have something called
an area exam where you're supposed to
read up papers
and i remember that rod brooks assigned
me lynn parker's earlier papers and maya
matarik's early papers and so
that's sort of how i got to know them
and cynthia brazil was a graduate
student there
hollyanko was a graduate student there
brian scazallotti was a graduate student
there
there's a huge number of people from
that group that are now
faculty in different places in robotics
but also
in other areas but but very much like
the
embodied intelligence kind of area was
was a big connection for me
and when did you finish your phd 2001.
and where did you go after that so um
at the time 2001 was interesting year
so my entire game plan was to go back to
bell labs
it was very clear to me i come to get a
phd i just hope that after i did all
this crazy stuff in my phd they would
still hire me
but 2001 all of the research labs were
tanking
and so xerox park which was my other
favorite place because they did modular
robots
um they were in trouble and deck was in
trouble and
everything was in trouble and microsoft
was just starting and google didn't
really
i mean google existed but the research
lab didn't exist so suddenly research
labs
were just not an option anymore
and so i also had my daughter when i was
a grad student so i had a little kid and
i was finishing my phd
and it was like this is so complicated
it's like i just want to defend
and then i'll think about what i'm going
to do with my life
but at the same time they started these
lecture positions at mit
because a lot of faculty were on leave
uh starting startups
so i decided that i'd be a lecturer for
a couple of years
um which is basically was a term limited
thing as a way of seeing if i liked
being faculty you know it seemed like a
good trial like i'd never
really tried being a teacher before
and i really liked it and so i did
research and i also taught students and
it was like
getting a little taste of what it would
feel like to be faculty so at the end of
that i applied
for faculty positions and i came to
harvard with one more detour
actually i have my career is defined by
detours
wherever it's like this is the path they
take a little short deviation come back
then a little short deviation so my
second short deviation
after the lecture position was to spend
a year
in a biology lab and this was super fun
so this was maybe the second
super influential thing for me was
systems biology and synthetic biology
was also rising at the same time and
they were really interested in
collective behavior
and they were open enough to want to
start conversations
with computer scientists and
mathematicians and physicians
and physicists and try to start bringing
everyone together to think about
important problems in biology
and so when i talked to them about
active materials and folding many of the
ideas had been taken from developmental
biology
and so we connected and the new
department chair there mark kirchner
said well why don't you come spend a
year here and he said you know bef when
you become faculty you'll never have
time again but
you know if you come for a year here you
know you'll influence everyone
will influence you you can do some
experiments and so i actually spent a
year where i
i and i say i tried i i tried very hard
to do actual experiments on the same
organisms that i had read papers about
and it was an
eye-opening experience it was such an
incredible that group
still is super connected
because even though all of us now are
are much more senior
and or sort of established in our places
there's actually a lot of connections
still between how we see
groups working together you know how
cells work together
so actually just this august one of my
one of the people who was sort of a
mentor
for me then we went and taught his group
how to program kilowatts
and so we had 20 biologists programming
robots and i was like this is just
heaven
but um yeah so that so i tried to do
experiments
and i learned a lot about how
biologists think about this problem and
also how hard it is
to reverse engineer a real system that
is is robust
is doing all the things you care about
but now you have to sort of infer back
what's going on
there's just limited tools you have to
sort of ask that question whereas with
robotics
we build it and then we still see things
we didn't predict
but at least you can now start to work
from first principles what happened
whereas that's much harder
to do to do in biology
and so but that discovery process
is very very similar
and you went into the computer science
department the computer science
department yeah
and were there other people doing
robotics there there were not
so it was kind of um so harvard has a
small group
but one of the defining features of the
group when especially when i joined
was that there's a lot of
interdisciplinary people so there's
david parks works in
economics and cs barbara gross who's
been well known in ai for a long time
works on human computer interaction
stuart schieber works on computational
linguistics
so when i said oh i want to mark on
computation and biology they were like
great
you know and that was not the reaction i
got from a lot of universities i went to
whatever i did was weird
um whereas here what i did seemed normal
in a sense
and so um so early on we started doing
some robotics and
my first robots were actually built off
of mindstorms so
or even literally the older mind storm
so lego robots were my first
robots um and i think one advantage of
not having a roboticist there was that i
didn't have to be too embarrassed
about it um there was actually i take
that back there's
rob howe is there and he does hands uh
but he
he did also a lot of surgical robots so
there was a lot of distance then between
what he did and what i did and i didn't
even consider myself a roboticist
considered myself an ai but over
time there's now a lot of people so
there's rob wood who does insect scale
robots
there's connor walsh who does
exoskeletons and
rob house so now suddenly we have this
robotics group whereas before
when i joined it was really sort of me
alone trying to
navigate my way of course i wasn't that
far from mit so
i could always go back and get you know
i have a huge network to ask questions
so holly janko and
james mcclurkin especially did a lot of
swarm robotics
at mit at the time so when i started my
faculty position
when i taught swarm robotics he would
bring over his robots
and teach a class
but in the process he really taught me a
lot about what
is important in thinking about robots
what things
bog you down what things are important
when you think of designing them so in
fact
many of the things that he wrote about
and talked about influenced what we did
with kilowatts because
you know he was the one who used to do
swarm robotics
and he had so many lessons that we
learned from so it was
it's really these interactions you don't
you have sometimes have interactions
because you're friends
so james is friends with both me and my
husband and
we just had things in common so we would
talk all the time and i was like i will
never have robots you can just bring
your robots
and now it's sort of the opposite we all
have lots of robots and it's
it's great now we talk about where are
we going with all our robots
so what was the first project you did
then with the mindstorms
so i'm interested in one of the projects
that
i've been interested in since the
beginning of my
faculty position is self-assembly but
self-assembly where
robots are building something so
inspired by how termites
build mounds so we did a lot of
theoretical stuff on that
and then our first implementation was
robots moving around tiles
and the idea was you needed only a few
simple sensors and local ideas to do it
so we thought well we should be able to
implement it
with something as simple as the lego
mindstorm robots it was sort of
also a proof right if you say these are
simple robots
well how simple is simple so the nice
part
about at that time especially about
using the mindstorms was that of course
you could change the body so i found
that
a lot of things that interest me in
robotics involve
the body of the robot and so you can't
just go somewhere and say i want a robot
that has this design and this arm and is
positioned like this
you just get a robot like a pioneer or
something something to move around and
look
but i always wanted something that could
move around and manipulate or climb
and those were always in the research
category those were never things that
were easy to buy
but with legos you could kind of build
whatever you needed to build and so we
basically built
the custom robot that we needed and it
was very complicated
and it used like two of the two
computers basically
or two of the mindstorm bricks and it
has a lot of parts
but it had an arm that could move up and
down and it had you know a gripper that
could close and it had lots of touch
sensors and
vision sensors and so we could actually
implement the whole algorithms just in
2d
and i think that way for me 3d printers
were
you know an amazing enabler so now i
have a lot more students in my lab who
can imagine things and then they just
happen you know we don't have to imagine
things
and then deal with the difficulty of
trying to make that thing
and the fact that it might be too heavy
or it might be difficult to machine
or it might take too much time or maybe
the kid the student doesn't have the
physical skills even though they have
the mental creativity they don't have
physical skills
to do it those used to stop a lot of our
projects before so we would just stick
with the legos
and now literally you know students come
and i'm like well let's
imagine that you wanted to make an army
ant that was crawling on top of army
ants and making a tower how would you go
about
what would be the design of that robot
what would that robot need to know what
would it think what sensors
and literally we can compose those ideas
so fast
so i feel like you're no longer
constrained
and and with 3d printers you can also
make many which is another
sort of fun thing you can go through
revisions but at the end if you have a
good design
you don't have one robot you can have 30
robots and that's a really
that's a really big enabler i think and
how did that research trajectory lead
you to the kilobots
well there's like more and more and more
right um
so so james mcclurkin
always had like this beautiful swarm but
then there's also a couple of groups
in europe that really have been in the
forefront of this and in particular
at epfl floriano's group and martinoli's
group
and dorico's group in brussels so
there's huge
they've always been interested in scorm
robotics so i sort of came to that a
little late
uh but many of them i met at conferences
and i talked and they
you know sort of they these
conversations where you run into
somebody like i ran into
martinoli alkira martinoli at
uh at an ai conference and we were sort
of
some of the few people doing robotics at
that ai conference
so we ended up chatting and then
five hours later we touched like
so many topics i had no idea that there
was somebody who was interested in so
many of the same topics i was interested
in
but that relationship turned out to be
really great so
they designed robots like the e-puck um
and his group was pushing numbers so at
that point they had you know
swarms of about 200 robots and that
robot was
one of the first robots i bought so i
bought robots
that other people had designed um in
order to use in my class or to use in my
research but you always sort of run into
this thing where
it's it's actually a great thing for
classes but for research you always want
to modify the robot in some
desperate way um so
we just sort of had lots of robots and
the terms robots are climbing robots so
those were
we sort of started with those um
but then uh mike rubenstein joined my
group as a postdoc
and his his thesis was was really
closely related to
things that i liked in my phd thesis so
we had a lot in common
and he thought about self-repair and
developmental biology and self-assembly
and so i thought he was going to come to
my group and we were going to work
on future sort of theoretical ideas
and we did for a few months talk about
various ones but at the end of a couple
of months he came to me he's like
you know actually what i want to do is
build a thousand robots and i was like
you gotta be kidding me
i was like have you seen the epochs we
can't like handle 20 of them
you've got to be kidding we have three
termies uh and
i was like okay well so what's your
what's going to make you succeed where
everybody else
failed because like the problems are
very real
but i think that just as james had
influenced me
james and other people had influenced
him so he had the list of problems and
we sort of started thinking about what
were the
what were the key issues that stop you
at a hundred
and i think just he just turned the
question around he said you know what if
we start designing by assuming
from the beginning that we'll make a
thousand we won't say we'll make 10 and
then we'll make 20 and then we'll
scale up we can only scale we can only
go backwards so we're going to start at
a thousand
so if you start at a thousand there's a
whole bunch of things you're just not
allowed to do
like you can't have manual labor in
designing these robots you can't have
too much cost per robot
you need a lot of things to be made by
pick and place machines
so once we started that trajectory
it was really more obvious
how we were going to get there but the
two sort of
pretty innovative ideas that mike came
up with were
using vibration motors instead of
regular wheels and also sort of instead
of using regular wireless or regular
sort of ir
into the upper part of the atmosphere
using reflected ir
is just a lot more robust so there are
lots of problems i knew
people had with robot to robot
communication so his technique basically
avoided a lot of those problems
and the vibration motors were really fun
i mean we'd seen a lot of the
bristlebots and toothbrushes and
so we put these things on but he was
like i think i've figured it out and so
we sit there and we'd bend the legs and
everything it turned out that we must
have spent
six months or so on the locomotion
strategy
not realizing that we actually had no
understanding of what we were doing
so finally after like every time he
would make a robot it would locomote
differently so in one case he bent the
legs backwards and the robot went front
in one case he bent selection and the
robot went backwards we're like okay
this doesn't make any sense so somebody
from another
group mahadevan's group suggested that
we put it under high speed photography
to see it
and then we realized that the locomotion
was really different from what we
thought and once
we realized that it turned out that the
vibration motors have a
have a bias so what he had done is in
one robot he had placed them
one way and another in another way but
they look symmetric so you don't really
notice that you were doing that
and that's why all our robots were
behaving differently so sometimes you
think you know what you're doing but
um especially with mechanical behavior
new mechanical behavior
often you know you really need to test
out your intuition otherwise
it's it's so easy to be wrong
so i think a lot of what i've learned
in in the last five six years is just
you know continually trying things
because you try them
and it gives you new intuition and new
ideas you you if you wait until
you figure you have the whole problem
solved you may have just missed
something that was really crucial to
thinking about the problem and i think
that's
maybe how a lot of roboticists feel that
the real world
is much more complex than
we want to give it credit for so or then
we want it to be
but sometimes it can go the other way
around that it can actually be easier so
the vibration motors actually turned out
to be easier than we had predicted you
didn't need to bend the legs at all the
legs were actually irrelevant
so it turned out you know we didn't have
to have any precision on how the legs
were put in
so it actually our life became easier as
a result of understanding what was going
on
so positioned as you are between sort of
biology and computer science and
robotics and
these sort of longer trajectories of
self-assembly self-organizing systems
bionics biologically inspired what what
does robotics
really kind of bring to this equation
and
what have you learned from these sort of
older histories well i think that
i mean there's so many i think that a
lot of
the areas that you mentioned you know we
think of physical system
and i think that in that sense computer
science and robotics
go very well together robotics is is
that part of computer science that
touches physical systems directly but
also brings in like
mechanical engineering and electrical
engineering what what are other ways of
thinking about
the same problem so i find that
if you think about programmable
materials it's not really a robot
it's not a robot in any sense of the
word that we might have thought
robot meant but it is an essential part
of robotics because it's basically
endowing
physical things with behavior and
to me i think one of the interesting
things is that i now look at everything
in biology as a robot
right it has behavior it has mechanics
it has physics it has
an interaction with the world and an
interaction and often the questions of
biology are the same how much comes from
the body
versus the brain or even if you were
thinking of cell how much comes from
physics versus a cell's active decision
to do something
so when i interact with with wendell lim
and we were teaching biologists to to do
robotics
what he was saying was he felt that
robotics
helps the biologists think
algorithmically
about cells what is the algorithm that a
cell runs
and i was just sir my jaw was on the
floor i'm like
he said the word algorithm so
i think it works both ways you know if
you think about biological systems as
running programs that actually gives you
a certain power
in thinking about biology as well
so i think robotics brings a huge flavor
to all of these fields and
and or and you can think of it as also
as ai
brings because it's the same concept but
the fact that there's also physical
there has to be a physical part to it
it's not
just what decision a cell makes it's
also about the forces of cells
experiences or
you know people will think about well
maybe there's external forces on
something and that causes the cells to
align
or maybe the cells are actually
measuring forces from nearby cells and
aligning
that's a big difference between what
kind of program is in the cell in the
first case in the second case and people
want to know the difference
they want to know if they have to
manipulate the genetic code of the cell
or they need to
manipulate just the environment and the
cells will correctly
heal something so a lot of these
questions are really
really important but they're kind of the
dual of questions in robotics
so i think now that i've had a taste of
building physical system it would be
very hard to go back
um you know and i think that that's
actually also
the part that robotics brings is it's
really fun and it's really tangible
and for me anything sensor networks
smart houses
all of these are sort of not so separate
from robotics
as you may have already heard like there
are a lot of people who work in a lot of
cross areas with robotics but i think
it's just
embedding computation into the physical
world in some way
is something that roboticists feel
comfortable with
and who are some of the graduate
students or post-docs that you've
trained and have gone on to do work in
robotics
so most of mostly in robotics i've had
very
recent students so michael rubinstein is
now a research scientist
as is justin warfell and um
kirsten peterson and but that's my most
recent set
so uh most of my earlier students
did ai more ai more theory
and some biology so we do work with
biologists as well
unfortunately we're running out yeah but
the wrap-up question is
okay what's your advice for young people
who might be interested in a career in
robotics
um
that's a tough one
just do it just do it you know it's it's
really fun
and i think a lot of time we spend too
much time worrying
about what we're doing especially smart
people spend too much time
worrying about what they're doing and
sometimes you just do it
and find what you like i didn't start by
liking robotics i started by liking
something else
and i don't promise that robotics is the
only thing i'm going to do with my life
i don't know
you know every year i'd like this is
what i love the most five years down the
road it's something different
so if you like robotics now do it now
why worry
about anything else
thank you very much