okay okay
so could you tell us a little bit first
about where you were born
and some of your first educational
experiences where you were educated
yes i was born in new york
but uh my parents were from greece and
when i was five years old we went back
to greece
and that's where i attended high school
uh but then i was
essentially a dual citizen and i decided
actually to come back to the us
to receive polytechnic for my
undergraduate
and then moved on to berkeley for my phd
i had a one year visit basically to
harvard while i was at
doing my phd and then i completed my phd
in 1998
and i've been at penn pretty much ever
since
so what did you do when you were at rpi
so at rpi
i did my undergraduate and then up when
i was about a junior i
decided that this area of sort of
control automation robotics is the area
that i would like to
do my further studies and at the time
there was a very
very very uh well-known internationally
renowned center on
sort of robotics for space exploration
it was the time with the space shuttle
and the robotic arms in the space
shuttle
so nasa had a center on space robotics
and
that was a draw for me so it was lots of
great people
at rpi at the time uh so then that's
so i did my master's there in the
in sort of a robotics layer not
necessarily space robotics
and based on my master's experience at
rpi i decided to sort of
move on to phd which then i
did at uc berkeley
and who were the people you worked with
uh so
the the my advisor at rpi was uh
professor costas quebecois so he was a
faculty at rpi at the time and then when
i graduated with my master's he moved on
to
become a faculty in greece in the area
of robotics and
and so on uh i guess perhaps what's the
position is why did i choose to do
control and robotics at the beginning
and
part of the reason i mean this field is
because it was a very difficult
course in controls at the sophomore
level it was a notorious course at rpi
and i was i took it as a challenge to do
well
and um there was uh while
when i was thinking the final it was a
fire alarm
and i had completed this was in the
middle of the exam and because it was a
fire alarm they said whatever you've
written will take into consideration the
exam is over
and i had pretty much completed the exam
in an hour and a half and then the
professor said
joe chao who's now the associate dean
there uh told me well you have two
options you can either do
controlling robotics or you can open a
greek diner so
being green so i said okay well i'm not
going to do the diner so i decided to do
robotics
instead nothing that worked out well for
me
so what did you work on for your masters
uh i was working
at the time there was a lot of attention
on the so-called parlor
parking problem that was the early
very early 90s late 80s and there was a
challenge of how to take cars which
cannot move sideways and how can
you do you know design algorithms that
we can do parallel parking automatically
so i think because
that presents a challenge if direct if
robots are omnidirectional obviously you
can move in all directions but if
they're not
then the question was how to design
algorithms for parallel parking so that
was
actually uh my master's project and i
went actually to my first conference
which
was a very empowering actually
experience because i felt that i just
want to keep on going to conferences
forever so how do i do that i go get a
phd
and then based on my experience with a
parallel parking problem i was looking
at other
faculty around the country that were
working on the problem and one of them
was professor shankar sastry at uc
berkeley so i decided to apply there and
that's how
i ended up at uc berkeley of course my
phd had nothing to do with parallel
parking but
that's how i got into uc berkeley and
that's why i decided to actually go
there
so what did you work on for your phd
yeah so at the beginning of my phd i
thought i would work on the parallel
parking and there were also very good
researchers
there doing focusing on parallel parking
and
and the mathematics behind the
algorithms behind parallel parking
but at the same time there was sort of a
a
new wave of research that was taking
place
in the area of uh hybrid systems and
hybrid control which is basically how
would you connect
high-level planning with say low level
control and high level reasoning
and low level control and one being
traditionally more
discrete or logic based or knowledge
based and so on
and the other one being much more
continuous and then there was
an issue of how do you bring these two
worlds together you know
half of the agenda was more on on the cs
computer science front and the other
half was a more electrical engineering
signal processing filtering sensors
so the challenge was how do we bring
these two things together and that was
a main research agenda at berkeley at
the time
there was a so-called path project at
the time which was looking at
sort of automation at highways how do
you get
cars to basically talk to each other you
know in order to reduce the spacing
between cars
and um and the similars i started
working on a similar project on air
traffic management how to sort of
think about aircraft to talk to each
other and do conflict resolution on the
fly
uh on their own without necessarily
having a central air traffic controller
doing the negotiation between
uh different aircraft so that has a
roots in
in robotics collision avoidance conflict
resolution
is a problem that has been addressed a
lot in the robotics
literature so there was a lot of things
that i think we could offer
to areas such as air traffic control and
that still remains to be the case so
from that i ended up going much more
towards
this area of hybrid systems of how to on
one side
have logic based switching whether you
should use this controller or that
controller or this maneuver or that
maneuver
so how to supervise basically low-level
controllers or low-level maneuvers
in a way to achieve a higher level goal
so that was in some sense
might the topic or the direction because
of my phd thesis
were you uh were you doing simulations
or were you working with actual robots
at that time
um i was not working with actual robot
i i was working mostly on
the algorithms and and also also the
how to verify and prove that algorithms
are correct
and the unique challenge there is that
there's people that
for example prove that their
say conflict resolution works well
and then there's people that verify
computer
science for example whether some logic
based switching is reasonable
for example if the obstacle or the
aircraft is within 10
meters or 10 minutes
of flight time away then uh
you know you do this evasive action and
so on and then there's this issue of how
do you ensure
that if you um put everything together
you ve you know you verify that the the
logic works you verify the controller
works but then when you put the two
things together
you actually ended up getting crashes in
fact if you look even at the recent
grand challenge
a lot of the crashes a lot of the cars
that didn't work was basically because
of the interactions of the software
logic with the controllers
so that interaction between the software
logic and the controllers is what i
ended up actually working on
not so much from an experimental
standpoint but much more from a
framework standpoint like how do we
reason how do we formalize our we model
and how do we verify properties of the
integrated system
and you mentioned that one of the
applications that you were thinking
about was the air traffic management
were there others
yeah so uh so during my phd i had looked
mostly at air traffic management and
then
since we since i became a professor at
penn
with my colleagues here at pan we also
looked at biomolecular networks so for
example
you can have the same kind of mixed
discrete behavior in biology
that for example concentrations of
certain bio
molecules can rise and then if they
across a certain threshold
then a certain gene activates and that
triggers it triggers another
continuous biochemical process and i
think there's a lot of work in that
direction uh and then a big emphasis
that i've looked at while i was here was
in some sense in the area
of what i would call multi-robot systems
so just like you have
uh multi uh agent systems in air traffic
transportation we have lots of agents
lots of aircraft trying to negotiate
for partly common goals like safety and
part individual goals like going to your
destination
you have the same issue in multiple
robots where you can have a common goal
like cover a particular area and then
the question becomes
how do you do that type of collaboration
among individual robots to achieve a
certain common goal
so um so i think a lot of the emphasis
of the work that we do here has been
based a lot on sort of the multi-robot
systems
and there i we do simulation work but
then with
collaborators in the lab for example
professor vijay kumar uh we're also
looking at experimental versions in
collaboration of the work
uh but the the the paradigm of hybrid
systems or mixing things
together is very broadly applicable now
even to
in the energy space and and you know
legged robots where you have sort of
bouncing and so on so it applies broadly
what were some of the people that you
worked with were at berkeley
uh my advisor was uh
shankar sastry who had enormous impact
on
both my personalities as a scientist as
well as a person as well
but they were also it was a really great
fantastic time it was sort of a golden
age of in in my area at the time uh
there's just there was just a
tremendous wealth of talent
synchronizing
at that those two three you know years
in the period of 90
to 2 000 uh so claire tomlin
who is also now faculty at berkeley she
was there at the time
um john leros who is now in zurich
was there at the time hashpanya who is
now at legacy santa barbara margaret
sagersted
at georgia tech uh
data cod bowler who is now a leader at
honeywell
actually working on autopilots and and
so on
uh kali johansson who is now
at the royal institute of technology in
stockholm
there was just a tremendous yi ma that
was now urbana there was just a
tremendous
wealth of talent and and i think that
was one of the
great things i really enjoyed at
berkeley that there was this sort of
environment it was not just an
individual or two but there was actually
an
environment and you kind of felt that
you just plug into the environment just
fantastic things happen and
and and you know you try to sort of
emulate a little bit that culture here
which we've tried to do
besides the people what are some of the
other important aspects of that
what you call in the environment yeah so
i think uh
so environment is both people having
really great people around you and
the other one is there was a little bit
there was a culture of collaboration
which was very important so it was not a
culture where everybody was trying to
sort of close off their ideas everybody
was really sharing ideas everybody was
sharing problems
everybody felt that they could go to the
coffee place and really think about that
problem and or its solution
and it was let's try to write a paper
together let's try to address this big
issue
uh so that was one one you know this
openness and the culture of
collaboration i felt was right
the other one was uh this culture of uh
being
thinking ambitious and being thinking
about big problems
and so i think that was uh uh
you know everybody was intellectually
ambitious everybody really wanted to
change the world and whether they do
that or not is the second thing but that
kind of culture if you want to think big
about how to
impact the world i think that was
another important thing
and the third one which is also very
important in my opinion
is this culture of mentorship that it is
not
just your own success but it's also the
success of your students
and i think that is something that you
create this culture where you're happy
with everybody else's successes
and everybody and your students
successes and you kind of celebrate
you know your students doing well rather
than just the research accomplishment so
you also celebrate the fact that you're
taking a person
from here through the phd that they
become
and i think that is very important
because at the end of the day you
you can do fantastic research but we are
in the education business and i think
you know that kind of
uh in the during graduate studies and
the phd programs where really education
and research
really mixed together and i think that
was really celebrated in a in a
very strong way there could you give an
example of how so
what is something that would display
that kind of mentorship
so i think that was very very so
when when when just to give you an
example when when people were going out
for on the job market for an academic
position i felt that
everybody was extremely well prepared
for the academic position and that it
was not just a matter of
of attending a meeting and going to some
meeting where they would say okay this
is what you need to do for the interview
and so on
it was actually the culture and by the
culture what i mean by that is that
uh for example the fundings agencies
would come to berkeley or a lot of
visitors would come to berkeley
and you would be exposed to all these
people and
uh and or when funding agencies would
come and they say this is where the
future of the field is heading towards
and there would be these meetings you
would actually actively participate in
these meetings
so when you came out you didn't just
come out as a student that was simply
had a research accomplishment but you
came out
as a student that had a research
accomplishment but also had networked
with a lot of very important people in
the field and also had network with
people in agencies that
were pointing to where the future is
heading and yet also network with people
that have very strong views about where
research and education go
so you you kind of inherit a little bit
uh that culture and and you you're being
very well prepared
even though you don't it's not part of
necessarily a formal process of
mentorship
it's part of basically just being there
and participating
and i think of course your advisor is
very important in creating
that environment where if visitors come
i remember i was there for a month and
and you know a visitor
very famous visitor came and and and my
advisor asked me like why don't you
present something
and and and you get constantly basically
into that culture
uh or you know funding agency
representative would come from nsa nsf
or dod and then you would actually go
for coffee with them
to discuss your own views even though
you're very young
about where the the future is heading
and you get
basically into this mindset of what's
coming up next what's coming in upstairs
which is not necessarily
the more traditional advising paradigm
which is okay here's your problem solve
it you get your phd and you move on
so i think that that i felt was very uh
that has really affected the way i think
as a researcher and they also
behave and operate as a researcher
ever since basically you mentioned that
everyone was interested in these big
problems
what were some of the big problems at
the time i think
i think at the time at least from uh
control robotics is
some of the issues where i mean at least
that i was involved were
uh how to think about large-scale
infrastructure
uh systems and and and and control for
them
so highway automated highway systems at
the time was a very big project
at least at berkeley air traffic
transportation
was also a very big problem uh so those
were the ones that at least
they were within my intellectual
neighborhood uh
and over time i think then it has
transitioned much more to sort of
multi-agent robotics and and then it
moved a little bit or sensor networks
and so on but that was
after my time at berkeley when i was at
berkeley uh
the automated highway project that was a
very very large project which had
lots of graduate students and so on and
um
and so that was that was very unique
do you have any idea where the
providence of the project is so was it
um
is it from funding or from somebody's
idea or what was the
burden yeah so the the i guess the idea
and the leader here is really professor
praveen varaya who has had another
impact in my own personal development
i mean the basic idea is that
infrastructure is not going to change
we're not going to dramatically change
or double the number of highways so the
question is how can we be smart
about the existing highways and and i
think then ideas from
automation robotics and control can
really help in increasing efficiency of
the
infrastructure and the idea was to take
existing vehicles and add some
intelligence on top so that they can
actually have much
closer spacing spacing that humans
cannot do on their own but you really
need computers to sort of
to monitor the spacing and control it to
very tight
and and increase highway capacity by
some factor of three or four so i think
that was the vision and then the funding
came predominantly from the uh
california transport
department of transportation call trans
and the there's the path project
and and that involves also collaboration
among on one side the robotics the
control automation people but also the
transportation engineers and so on
so that was also another unique feature
that it brought all these communities
together
um now the the the project uh
mature technologically and it went to i
think in some time in 1996 there was
sort of a demonstration
at the uh highway 5 at uc san diego
and where they were actually able to
show a platoon of vehicles
doing some kind of like a soft train
with very tight spacing and so on
so technological viability and
feasibility was demonstrated
then from that point on the question is
okay okay
make a transition transition that in
real highways and so on
and of course the answer is you never
really transition the full-blown idea
so so i think what has happened of
course
is a lot of components of that big
vision
have been already introduced in a lot of
our vehicles so for example
if you see or what's happening in the
automotive industry a lot of the
features they talk about is adaptive
cruise control or intelligent cruise
control
or safety warnings and a lot of those a
lot of that technological feasibility
really goes back to large projects like
that
which of course were not implemented at
full scale
by by say transportation departments
but a lot of the components are making
their way into the automotive industry
right now
and i think volvo i believe last year or
last month is introducing some of the
spacing
actually controllers in their next
lineup of volvo cars what were some of
the interesting questions in terms of
robotics
i think collision avoidance conflict
resolution
uh how to um so there were two layers
uh of of uh planning
there so there was a layer of planning
of how do i go from here to new york
which highways do i take how do i make
lane changes how do i enter the highway
how do i exit the highway so this was
the more
is the more discrete level of planning
and control and how do you make
potentially change to the plan if
something happens
with failures and so on so that was one
layer and the other layer was the more
low level
which is how do you keep your distance
to the car in front
what kind of information do you exchange
with cars in front and behind
and what is the information
infrastructure that you have to share
with you know robotic cars you know
in this kind of string of vehicles in
order to sustain
the kind of desired spacing the exchange
position information do you exchange the
lost information
relative velocity relative acceleration
what is the level of effort what's the
level information
and how often do you need transmit in
order to have very tight spacing
uh to do this and then the question is
if a car wants to enter or exit the
highway then how do you break the
platoon how do you get out
and so on so that was on the automated
highway side
i think on the on the similar kind of
problems arise in the air traffic
control
arena where the the the principle at
least at the time
was to do free flight which was that
every
aircraft would sort of uh choose its own
path
uh without using the sort of the grid
the virtual grid that's on top of the
united states and sort of navigating
because now you have gps you can pretty
much fly anywhere
and then the big challenge there is
conflict resolution if two aircraft or
three or four
especially as you get closer to airports
get in sort of head-on
collisions uh or or any type of
collision then
what are the algorithms by which you
sort of avoid collisions resolve
conflicts and so on
and i think that is very much a problem
that people in robotics have looked at
a way you know with robots in multiple
robot settings
or a robot avoiding obstacles of how to
avoid
obstacles and so on so so there was a
lot of exchange at the time between
the air traffic control and the robotics
industry the problem with the air
traffic control industry compared to the
automotive industry is that it's highly
regulated
and it was a lot more it still is a lot
more challenging to get sort of
technological advances
into the regulated faa system as is
whereas in the automotive industry
is a little bit easier to get not the
full blown agenda but
parts of it were able to see any of your
work implemented uh no that's very hard
but you can what you can
there were pieces that were uh there was
a darpa
sec program uh which
was taking some of the conflict
resolution uh work that was happening at
berkeley
and they were able to demonstrate it in
some experimental
uh flights with just two aircraft so so
that again technological viability was
shown
but to make that sort of a full-blown
full-scale
project at faa that that was much harder
and of course now
with president obama next gen initiative
a lot of those ideas maybe not that the
full-blown free-flight uh
agenda are sort of resurfacing as how to
make it at least more intelligent and
how to help
air traffic control advisors make
intelligent decisions and
reduce their workload while making
airlines a bit more efficient reduce
fuel and so on
and so was this all still work that you
were doing during your phd
yes and a lot of this was of course
within collaboration with people with a
lot of the people that i already
mentioned
and um and a lot of it is in some sense
still going on by some of
the collaborators and so on yeah so what
did you do after
yes so when i came to um to
pan as an assistant professor pen of
course is a very
uh internationally renowned place for
robotics
so it was very clear to start working a
little bit on
what where the new challenge is not
necessarily in air traffic or
or in automated highway systems but
actually in robotics itself and
uh it was uh it was clear that
that um i mean at least there were two
major themes evolving one was
uh going from a single robot to multiple
robots
i mean that transition was sort of
starting in in early 2000 and in some
sense it's still happening now
and then the other one is to sort of
look at connections between robotics and
and biology and i think there's multiple
dimensions on that front um so
so then uh the going from a single robot
to multiple robots was very well aligned
in some sense with the problems of
uh intelligent cars on a highway and
intelligent aircraft so
so i think that was a uh with a
different focus that was a
new set of problems that we decided to
work on while uh
at penn and a lot of us at penn actually
in the grasp lab are working on
on different aspects of the multi-robot
coordination problem
and um so more specifically the problems
where
uh things robots are different they they
typically have a team of robots
they're smaller so they're more
disposable more
unreliable but
compared to air traffic control they
typically have a common mission
so they want to do coverage or they they
want to cover a particular area
or they want to collect information say
over a search site and so on
and then you have many of them and the
question then is how do you
on one side coordinate so the coordinate
is who does what who talks to who
who and what kind of information against
they they exchange with each other
and then the ques that's the higher
level than the lower level is
how do they go out and they do say
trajectory planning control
and so on so that they can achieve the
common team mission
and and how do they go from team
objective to individual objectives
is the kind of main challenge in that
topic
and and i think that has brought
robotics in some sense much closer to
areas such as communications and
networking
information uh flow and so on and signal
processing
um and i think that that that was
intellectually very appealing and
and so on uh the other
dimension that was happening uh is that
there is a growing interface between
robotics and biology on many fronts
uh one is that people look at
bio-inspired robotics
and even that is has multiple dimensions
so you can look at bio-inspired robotics
from a
locomotion perspective for example dan
kodick is working on how to look at
cockroaches and make a sort of
legged robots with very robust
locomotion systems so and so on
then there is bio-inspired robotics even
at the coordination level so for example
you could look at a team of ants how do
they cooperate to achieve a common goal
like searching for food and how can
those principles
be translated in the multi-robot agenda
so that was a
that's another dimension and then
there's a third one was a bit more
exotic and a bit more forward thinking
is that um uh i mean if you look at
robots the way we build robots right now
is basically out of mechanical and
electrical parts
and so we programmed them to sort of
achieve
to get these anthropomorphic robots or
the cars of the intelligent cars or the
you know the irobot uh rhombus and so on
but
as we understand a little bit biology
better
at both the organism level but also at
the cellular level
i mean people are starting to explore
also how to build the
sort of robots at the cellular level so
rather than thinking of silicon
as a substrate by which you build robots
now you're looking at basically building
cells that you can program cells to act
like biosensors by actuators in
biocomputation and they can perhaps
you know go inside your body and send
certain chemicals and then you know
possibly interact with uh with the
environment in a way that
uh typical robots would do in the
mechanical world but now we're starting
to think about if you like robotic
biology
making biology even more robotic and so
on and that's a bit
more forward thinking but that's another
thing that has evolved over
my experience here and
in in doing and this is research that
you were doing or research that was
yeah so so uh uh yes so i think a lot of
this research has been done
with a lot of my the members of my group
a lot of my students my former postdocs
and former students
uh but also in collaboration with a lot
of the other groups in the grasp lab and
i think
as i was saying before at grass club we
feel we do have a little bit of the open
collaborative
culture that we that we saw at berkeley
uh both among different groups
that bring different expertise so one
could break the algorithmic one could be
experimental or one could be a little
bit more bio-oriented or
one could be a little bit more
mechanical one could be more electrical
and so on
um but also with the mentorship culture
and i think
that you know we really try to
uh to the success of our students is
basically our own success so
uh we try to promote that open culture
also to our students so that they can
collaborate with each other among
different groups and kind of
pursue all all these things so obviously
we contribute different parts in
different pieces
but but i think the important thing is i
view it really as a collective
accomplishment among my group but also
among other
with other groups as well who would you
say
are your closest collaborators for
a lot of the multi-robot work
it would be vijay kumar on the on the
more
robotics and uh and experimental side as
well as the algorithmic side
and then on the algorithmic side it
would be mostly professor
ali babai who is working on more on the
mathematics of
multi-agent systems
on the biology side
again a lot of this work has been with
professor
vijay kumar but also with some
biologists so the biologists are
from the cellular level at penn it's
harvey rubin in the school of medicine
um but also um
other folks at the organism level so i
i'm the pi for
a biologically inspired project
which involves both lots of engineers
robotic engineers but also lots of
biologists
and the idea there is to look at
biologically inspired
principles of cooperation and how for
example transitioning a lot of the ants
work
into for example collaboration enhanced
to collaboration of robots
and and there the interesting thing is
even when you get different biologists
together even different biologists
don't really the ants people don't
really talk to the dolphins people and
so on so there's a lot of
interesting intellectual discussion
happening even on the biology side
and then you have the interesting
interaction between
cooperation principles in biology and
cooperation principles in engineering
what's different what's common and what
makes sense on one side what makes sense
on both sides and so on
but of course most of the collaboration
is really with your own students and
your own postdocs who have now
i mean some of them of course here but a
lot of them are now
on to faculty positions elsewhere
who are some of your early students
who've become faculty
so uh paolo tabwada um
is now at ucla uh is an associate
professor at ucla
uh in the area of multi-robot systems
one of the
uh prime works on uh flocking
off of
you know when you look at sort of the
movies like uh you know
lion king and you see lots of lions
flocking down so a lot of the flocking
work uh
is based on at least some of the
algorithmic guarantees that we have
shown is based on the work with ali but
my former post-doc
herbert thunder who is now at the
university of delaware
um a lot of the work on um
human uh sort of i would call it more uh
so so another vision another challenge
that i see going forward
is how would we
interact with robots at a very high
level of abstractions especially as
robots
get into the broad society and you have
everybody interacting with robots we
cannot be programming robots using c
or or or java we have to actually be
able to speak to a robot
and that would be the way that that for
example you would want your kids
or your your parents or your
grandparents to potentially interact
with the robot
so the question then is how would you
speak to a robot and i think that
that area of human robot interaction
and higher level interaction mechanisms
that would
you know would make robotics a bit
penetrate more and broader society are
very important
for the field and there we have done
some work with
hadas cress gazette who she is now
at uh at cornell university and and
she's following up that agenda of
you know kind of the knight rider how
would you speak to a robot and the robot
would actually do what how would you
understand what you're saying
on one side then how would it execute
exactly what what you're doing so i
think that's another
um main topic that i see going forward
that hadas has
really i think pioneered um but then
there's a lot of other
um collaborators uh
agung julius is now faculty at
rensselaer he has
really worked on the interface between
robotics and biology and how to
and uh and in collaboration with salman
sakhar who just won this award
who is now actually postdoc at mit um
there's they have you know there's quite
a few members so that i feel very proud
and blessed that they were actually
members of my group
yeah so you mentioned a lot of different
um
types of products looking
were there particular applications that
those were related to
yeah so um i tend to
work mostly on the how to think about
robots rather than building them
and but but my thinking is always in
collaboration with people that actually
uh build them so um there are some
uh projects for which we had to do uh
demonstration for example in the
multi-robot space we had to develop
algorithms that
you know others then developed robots
that uh
flew them uh in the the work we did for
sort of talking to robots
we applied it to the darpa urban
challenge in the semi-final run to see
that the algorithms would actually work
in that setting
again we didn't
use our algorithms during the run but we
actually
post uh post challenge after the
challenge
uh concluded we actually took the
semi-final run and
and so we always ground our uh
our algorithmic developments on on real
problems
in fact i don't like to ground our
algorithmic developments on other
people's algorithmic developments
i really wanted to ground on unreal and
then a lot of the
real applications happens typically
excuse me in collaboration
with others because i don't consider
myself to be a very good experimentalist
uh but they're much better people on
that uh in the grasp lab
but i do interact a lot with them um
so yeah so what inspires the particular
real world problems or applications
um i tried
to talk to experimentalists as well as a
lot of the industry
people as well as people in the field
that use robotics
for example in the case of in the
department of defense
funding uh i tend to talk to a lot of
people that actually use these systems
rather than people that develop these
systems as to what do they see as as
problems and and from that i usually
draw motivation
uh about the problems that i work on and
the problems i work on are usually on
the algorithmic side
however i usually draw motivation it
from people in industry that say
this is a big problem for us to me
that's very important
um to to
sort of acknowledge the the importance
of the problem and that there is sort of
a market for the
solution of such a problem uh out there
and but i also need to make sure that
that is a problem that would lead to
that's the market side but then you know
we are all in the phd training business
so
at the same time you have to take that
problem and select parts of it that
would lead to good phd thesis and
good careers for the students and not
all problems
that come from from users or from uh
or from industry necessarily are good
academic problems
and uh and vice versa so
and i think that that is usually a
challenging part
identifying problems that have
importance
in a broad setting but also
academic interest so that if people
address them
they can go out write nice papers and
presenting conferences and on one side
convince your academic community that
this is a very interesting elegant
problem
but at the same time it's at least
motivated by a problem of importance
rather than identifying an intellectual
whole and going and covering it without
necessarily identifying whether that
whole has a need first and so on
and so basically a lot of it comes in
in discussion with industry in
discussion with
um
with you know funding agencies a lot of
these industrial workshops where they
they basically clearly say well you know
we need
uh ways to uh say
interact with robots at higher levels
now of course how you translate that
down to a more technical challenge is a
challenge in its own right
uh but i think it it it points you in
the right direction because
identifying the right direction i think
to me is half of the battle
because uh after you know all lots of
everybo
lots of faculty are very intelligent and
lots of students are very intelligent
i think it's really pointing them in the
right direction that that that is the
you know half of the battle because then
you know that their talents will
will address the problems that that are
in front of them but it's really
pointing them in the right direction
and in the right direction also means a
problem that has
long-term professional growth for the
students
which is another dimension one has to
consider as well
in terms of the big problems that
robotics faces
which do you see or the ones that if we
really made progress on them could
really advance robotics
yeah so i think since we already
mentioned i think this issue of
what i call human robot interaction
is is is very important um
it is perhaps uh underattended
it's a it's a difficult problem to even
formulate
and i think there we should be talking
on the other side of the human robot
interaction which is potentially our
friends in cognitive science and so on
and i think that's very important as
an area uh the interactions with biology
i think will be strong at
multiple levels whether that's from
the biomechanical level all the way to
the
bio organizational level or
to the cellular level where we think of
cells as robots and now we try to
program cells the way we try to program
robots
i think the
however i think
one of the major problems in robotics
that i think is from there from the
beginning and has not been really
addressed in a satisfactory way
and is that robotics is in some sense
very well aligned with what i would call
the science of
integration and the science of
integration basically is that if you
look what a robot is it is essentially
uh you know you have a legged robot or
you have an
automotive car or or an aircraft or an
unmanned area vehicle or a manipulator
and then at some point you have sensing
you have actuation and you have
computing
and how do we integrate sensing
actuation computing
physics software in a way that
that it is fast and rapid and reusable
and modular is in some sense not well
addressed
what part of robotic science
is platform dependent
what part of robotics is platform
independent
i think that's what are the platform
abstractions
like the way that tcp was able to be an
abstraction for communications
where people could develop applications
for networking
and then people can make networking
faster so what are the parts
of planning algorithms say that would
work
for both cars and aircraft and what is
the part of robotics that really depends
on the physical
particular aspects of this robot or that
robot
and i think my concern is that every
time we have a new robot we are doing a
lot of vertical integration we
have the robot we build quality spheres
we build controllers and then we build
the hierarchy all the way up
and then if we go to a different robot
we do exactly the same thing
and how do we move laterally
accomplishments
from one platform to another
is very important for enabling
what i would call the science of
intelligent machines because it's not
clear what part of the science
is platform-dependent and what part of
it is platform independent
and i think that to me is at the center
of robotics in the intellectual center
for robotics and needs to be
addressed uh by a larger
percentage of our communities do you
think that and and if i can understand i
think there
there are also opportunities to to
discuss in other sciences that have
have achieved this type of uh of
uh hierarchical or cross layer
so networking is a discipline that was
able to come up with an architecture
that has served them well
um in in the area of computer science
people
can talk about modularity and reusing
software components without necessarily
rewriting the code
from scratch all the time uh now of
course there are unique challenges to
robotics and that is that this robot may
have two legs or four legs
or or be flying so at some level they
have to be different
and you also are dealing with an
environment that is
uncertain and i think whereas in a lot
of these other domains the environment
is very well controlled
but i think that that is the central
question that needs to be addressed
do you think it's going to be in the end
addressed more through standards and
we're trying to say open source
i mean standards are are important
and and they identify interfaces but i
think if you do standards too quickly
it can also uh be a a problem
and and um uh so ideally what i would
like to see
is the following thing i mean you buy a
robot
you and you can go on the web and
download the kalman filter and
controller and within
a day you have it working quickly
so you shouldn't have to write three
papers and have two students working in
two years to do that
so if we can do that then we will know
what part
of of robotic development is platform
dependent and what part of robotics is
platform independent
do you think willow's robot operating
system is somewhere in the right
direction
i think that will help in the
uh in making a lot of the
integration software integration
challenges
uh more robust and faster and better and
will
be more available now whether the the
what what is probably missing
in the is that when when should i use
this kalman filter versus that kalman
filter
and i think that type of so so one
should enrich
such uh projects
with um some kind of a
process by which i know that if i
download this piece of this module from
the robotic operating system
i know that it will work in these
situations for these types of robots i
priori as i download it
and that type of type checking i think
is important
uh to have some kind of faith that what
i'm downloading will work
it you know otherwise you will quickly
integrate it and you will test
whether it works or not but if you want
to have any faith or confidence that it
will work well
during the rapid design time i think so
i think that is really important
to to quickly assemble intelligent
systems
or quickly reconfigure intelligent
systems as the
say the environment changes or some
sensor fails or some actuator
is uh you know uh installed
on the on the robot i think yeah so i
think that that to me is the central
question
that i see that that everybody deals
with but nobody addresses it
head-on in some sense yeah
what do you think is are some of your
most important contributions
to roblox um
my students i think i actually believe
that very strongly
um but uh and so
um if i look at uh
i'm just gonna say three so i think the
work by herbert tanner
on the flocking uh i think
and uh is a fantastic contribution
i think the work by hadas chris gazette
she's working on sort of
linguistic control of robots and how do
you take
english sentences and you translate that
down to
robotic plans if you say so for example
if you say something like
go behind the building and and look for
survivors and how does a robot take such
a sentence and
and translate that down into a
trajectory and knows what's behind and
what building
i think that type of automation at
higher levels
uh connecting higher levels of reasoning
and and
communication to low level execution i
think is uh
is important and it is related to this
science of integration of how do you go
from high level
potentially platform-independent plans
to low-level platform-dependent
executions
uh and then i would say a lot of the
work on the multi-robot
systems where you can view robots not as
not so much as robots that move things
from here to there or they go somewhere
but actually you think of robots as
actual communication agents
where a robot you have a team of robots
and a lot of them for example can spread
over an area
to say provide a communication
infrastructure network so that
one robot can talk with another robot
via other robots so robots need to
in a distributed way reconfigure to
route information from one robot to
another
i'm just naming three this was by the
phd work of michael
zablanos uh but i think uh
i'm very proud of my students and for
every student that can sort of come up
with
such a story the three is the magic
number two is the number okay um
has have you had public response to your
work and
what has it been
yeah this might be a difficult question
you know
you know actual applications that are
very imaginable
yeah um
uh most of the kind of the
the uh the public uh awareness of
of this type of work usually comes from
the angle of the department of defense
funding
and as such it typically comes from
what i would call search and rescue
type missions or or so
so i think uh there has been you know
press releases and so on and
and and and outlets in the media that
have looked into this
but a lot of the times it really comes
from with a kind of a military
tone uh which i sometimes have very
mixed feelings because um
it's not necessarily focusing so much on
the scientific aspects of the work which
of course could be very broad and very
dual use and so on but really looks at
it from
from a military viewpoint so i think
others in the grass club have had of
course
tremendous success because the usually
the
public awareness comes not so much from
the thinking about robots but actually
the application of robots in the domain
so i think in that setting
of course uh you know vijay kumar and
dan kotick and so on other and others
have had a lot more
um public um
exposure of their work in some sense i
don't know i don't think that's the same
definitely
i mean how do you envision if you do the
social relevance of
robotics research there's two aspects of
it
um i mean robotics always i would i
would like to decompose the robotics in
two parts
one is the technology of robots and i
think there when you look at the
technology of robots you
are really looking at the robotic
platforms you're looking at whether this
is a
an automated car or whether this is a
vacuum automated vacuum cleaner or is a
manufacturing
manipulator and so on i think there
um the the social relevance is much more
its impact on on industry social welfare
economy and and so on um
jobs created and and and i think there
are some
you know i think robotics may
very well be in the sort of the
i would call it the ibm to microsoft
transition of the late 70s where
there was hardware garage style hardware
but now
with sort of ros like investments and
serious
operating systems and with
building sort of an application layer on
top of robotics one could see
sort of a re-emergence and resurgence of
lots of applications of robotics and
lots of
of the of the platforms of robotics so i
think that
i think we could be sort of in the
middle of a transformative
period for robotics uh from the platform
point of view i think
and then there's what i would call the
science of robotics
and the science of robotics is
everywhere i mean it really is
everywhere it's in the unmanned vehicles
that
northrop grumman has it is it it is
going to be actually in green buildings
uh it's going to be it's already in next
generation automotive systems and so on
so i think that's not necessary i think
one of the challenges in robotics is
that
in the broad perception it has very much
this anthropomorphic
image and that is a good thing and a bad
thing so when you say a robot to
somebody they know exactly what you mean
but the bad thing is that they know
exactly what you mean
and and you know and i think uh when and
i think
robotics needs to sort of uh claim a lot
of the
intellectual successes that have
happened in domains that are not
necessarily anthropomorphic
uh which are sort of areas such as
unmanned vehicles potentially
automotive systems air traffic
transportation and so on
which use a lot of the components of the
science of robotics but are not
necessarily applied to what people
perceive
as robotic platforms uh so
the one has to basically view in both
angles
why do you think there's a rising focus
on human robot interaction this may be
just a
personally interesting question to me i
think part of the reason why there is a
there is a need is because i do think
that the level of automation
robotic platforms has moved up
so i mean let's say in the 70s you would
be doing a
simple robotic go from point a to point
b
and then and so on and i think now we
are basically able to have levels of
intelligence which is basically go to
new york
and i think now you're getting at a
level of intelligence or or
that machines are getting at the level
of intelligence that it's a lot easier
to basically be at a communication level
with a human
and i think with with the broad public
you can have a lot of people programming
it using c
of going point a to b but as we get
robots
more and more and more intelligence what
has happened over the 20 years i think
now we're at the stage where i believe
robots and humans can actually really
interact because the level of automation
robotics
has really increased to the point that
we can actually plan at a very high
level of abstraction at a high level of
abstraction that can actually be very
close to the level that humans interact
with other humans
so i think that is to me at least why so
it's really the
progress that has been made at the level
of increasing robotic autonomy
that we can actually now start thinking
about human robot interactions
the way that that that would be
meaningful for humans to actually use
robots and program
robots the way they would speak to
another colleague or another friend or
family member
so i think that that's why i think
investments are needed in that area
so that robots really penetrate broader
society
and i think that could be a tremendous
way that by which
not just robotics but broadly automation
and intelligence is much more pervasive
uh you know in our homes and offices and
so on
you directed the grassland yes grass
for a while could you tell us a little
bit about that experience and how you
think the lab has changed through time
um yeah so i was uh the director of the
breast platform
for three years into from 2005 to 2008
and uh the the emphasis
i put as a director is to
uh help uh collaborative research
efforts
so i think the main emphasis was to sort
of look at collaborative research
opportunities
and and increase the
uh collaboration within the lab not that
that it was already
very high but it was actually more going
out and getting a lot of research grants
proposals
and so on and of course that was very
well aligned with what is really
happening in
in the uh funding uh agency so which is
transitioning from a sort of a single pi
to multiple pi
uh funding model in some sense so uh
another so that was one major objective
the other one which i'm sort of proud of
is that we started the uh masters in
robotics
which is the second masters was at that
time was the second masters
which was a very focused program in
robotics and uh
and it was very unique because areas
such as robotics are
very hard to do from any particular
discipline whether that's electrical
engineering mechanical
general computer science and so
there were lots of students that
basically said that i want to do
robotics i don't want to do
computer science i don't want to do
electrical i really want to do robotics
so there was a clear
educational need there and
so we started a program which is
administered by the lab and
um and it exposes students to sort of
the right balance
of electrical engineering mechanical
engineering and computer science
bringing in a sort of ai and and control
and computer vision and mechatronics and
so on
and uh and uh it's now stabilized to
you know it's we're still in growth mode
the 40 to 50 students
and i think that is another way by which
the lab can have
impact because at the end of the day we
as i was saying before we are
an educational institution and our goal
is to
not do research or education in
isolation but rather coupled to
and such programs enable you to take the
research excellence and sort of
convert that into education yeah so i
think that the
the industry is very important for any
educational program in my opinion
especially for a master's
more targeted program and especially for
a program that's new
i think that's very important students
you have to look at it from the student
side
they come to a program it's a new
program they want to know that their
future is
is both interesting exciting and secure
and
um so they uh so i think
we absolutely three ways either they go
to what you would call uh robotic
industries like irobot and so on
or they would go to uh other industries
that may be broadly robotics you know
they're interested in the science of
robotics not necessarily the platforms
or robotics so they could be sort of the
lockheed martins and so on
and then a lot of them also continue
they're inspired and they continue on to
do
phd either at pen or elsewhere so i
think
and we have we're doing extremely well
with the program so
so that was another um aspect that i
focused on during
being the director and the other one was
uh was just broadly
making the world a little bit more aware
the academic world in particular more
aware about what's happening here
by having you know very distinguished
speakers and so on and i think that
that is uh uh
that's to me very important education
for everybody i mean the faculty
the students because you can take
courses and you can do research but you
also have to
uh network your community with the
external community so that they have a
very
important sense of what's happening
externally
so um and and i think now the the
the lab has grown tremendously uh over
the past
five six seven years also in the
while vijay kumar was the director as
well as kostas
and i think now right now we may be
producing actually the most phd students
than any other institution in the area
of robotics in the united states
how many of you we have about 70 to 80
phd students
and i believe cmu has a similar number
you have a lot more faculty a lot more
faculty yes
yes but if you look at it from an
educational standpoint and and if you
think that your impact is through your
students
then i think yeah i agree so so if you
think that impact
cm is a larger place and they have much
many more researchers
but when it comes to uh students and
getting students out there uh
at least phd students we we are about
comparable right now
so since we're unless peter had some
other question that you'd like to ask
since we're at the students should we do
the
closing statements our general closing
statement is
this question so if um do you have some
advice that you would give to young
people who are interested in robotics
uh so my advice intellectually would be
um focus on heart problems
uh and there are certain heart problems
that
um have been with us from the beginning
and there are certain hard problems that
emerge so for example i would call the
science of integration as a
problem that has been us from the
beginning and i think their heart
problems
uh uh like human robot interaction
like multi-robot systems that are
emerging or connecting to biology and so
on
um the other one is
um uh you should have one leg in
robotics and one leg somewhere else
be it be interdisciplinary and not that
has its
of course it sounds great everybody's
doing it but there are people that
really do it know that it's very
challenging
uh you have two communities to make
happy their metrics of success in
different communities very different
but the reward can be very high both
intellectually but also from a career
standpoint
so i think that that way so whether
you're you want to do robotics in
biology
or whether you want to do robotics and
networking or whether you want to do the
sort of those types of interactions
or robotics and cognitive science i
think that's where some of the bigger
problems
as robots become more and more
intelligent
more intelligent we will have to embrace
more and more disciplines
outside robotics which focused more on
the platforms
but as we go higher in intelligence
we'll have to talk to cognitive
scientists
as we go by you know more sailor we'll
talk to biologists and so on so
so you have to be intellectually open
and in some sense brave
to to make that transition
and that may delay your phd it may
be difficult at the beginning you may
feel that you're slowing down
but but i think in the long term
it pays off third i think it's pick your
advisor well
i think this is something that i have
learned
i think a lot of students pick projects
well
but i'm not so sure they pick advisors
as well as they pick
projects they're extremely excited
because they really want to do
this great project which is you know and
so on but
but but being with an advisor uh and i
you know i've been on the big advisor is
a very special relationship
uh the mentoring you will receive from a
great advisor is by far the best lesson
you will
have the most important lesson of your
life but potentially
uh the professional life and i think uh
so i think there one has to look at at
advisors that have very good track
records of advising and so on
and i think that's something that i
would think i think that
students should be when they make
graduate applications they should also
they should be looking how great the
projects are
but they should also have try to
identify or estimate how good the
advisor
has been or will be and so on
because you can be at a great place in a
great prior project but if your advisor
if that chemistry is not good and if the
advisor is not
looking at your own interests as
the metric of success then there could
be conflicts and and so on
um i don't know what else
would you pick a good advisor especially
you know when when you're a student and
you're in the program
yeah so i think for i think for for more
established research
researchers and and so on you could sort
of look at their track record
and and whether they uh
whether they a lot of their former
students are in academia or industry
leaders
or whatever depending on what your goals
are you should be looking
now that's a little bit unfair for more
young
faculty so but for for more established
faculty i think that's one thing
for and for established faculty and i
think you can do is you can look at
their students and you can
potentially ping them and ask them for
how was it working with x and y z and so
on and i think
that that that could provide some
meaningful advice not everybody's going
to have the same response but that could
be the mechanisms that you could pursue
and so on
um now that's not fair for
young faculty that are just starting and
they may or
may not have a track record so um
so i think there you're just gonna have
to go with a gut feeling sometimes they
could be this could be the most exciting
advisors because they're full of energy
they have
lots of ideas they just came out of
their phd or post doc
and and so on so i think there you're
just gonna have to make a little bit of
a judgment call
and you could also see a little bit the
surrounding culture so for example if
that person is in a
in a center or a lab where there is that
type of a culture then
that type of culture will sort of spill
over from one person to the other
um another thing is you can look at
people's collaborative credentials so if
you see that they write papers with
others
then you can have a good sense that
these people are open what if you see
that it's basically my student and the
faculty advisor
and all their papers are that then it
will be more of a pairwise relationship
between you and your advisor
so depending on what your goals are i
think you can sort of
try to look at hints of of that when you
pick an advisor
i think i i think picking the right
advisor for me especially with
extreme is can be a completely
life-changing
experience and and so
i was very fortunate to have tremendous
advising
advisors and mentors in my life so