so i'm just going to start by asking
you where you were born where you grew
up and where you started your education
okay just start talking yeah
or introduce yourself okay so i'm stefan
shaw make sure i look at it at your face
normally i'm gonna try to be pretty
close okay
so i'm stefan chal and i was born in
germany in europe and i got my prime
education at the technical university of
munich where i did my
diploma and my phd in mechanical
engineering and artificial intelligence
and i simultaneously doing my phd also
was at mit
where i was in cognitive sciences and
artificial intelligence
so when did you first become interested
in robotics
um i think during mechanical engineering
i liked dynamics and robotics already
very much
so it was kind of i like moving systems
in in controlling movement systems
and then when i got in my phd i got
increasingly more
interested in how to build these systems
and also how to add intelligence to them
which kind of added the artificial
intelligence component and then with mit
when i was there was essentially there
was a brain in cognitive sciences in the
artificial intelligence lab at mit um
there when i went there was the late
1980s
this is kind of where the new wave of
neural networks
and computational motor control and
kind of intelligent robotics was created
to some extent at least there was other
universities
as well of course and with this kind of
excitement i got very interested in
robotics as well
actually i got interested in both
biological motor control and robotics at
the same time and trying to understand
the biological components from a
technological viewpoint
and the other side try to understand
from biology a little bit what we should
be doing in technology
so what was the first robotic project
you worked on
the very first robotic project
so when i arrived at
mit so i started to work with chris
atkinson who was professor at mit at
this point
and the first thing we did is
essentially work on little juggling
machines
so the very first thing i did was
actually a
a five ball juggling machine um
which was which is called a bounce
juggler so the balls basically bounce on
the floor once because they come up
and it was originally invented by claude
shannon actually
so shannon had a lot of toys in his
basement or wherever he's kept them
and he liked juggling machines so we
basically created a three ball bounce
juggler and we basically created a five
ball
bounce jack it was a very primitive
robot but i tried to make a point that
you can create some very complicated
behavior with an extremely simple
machine actually it had no computer at
all it was just a machine
and then later on we started to do more
intelligent machines which were bouncing
balls and tennis rackets and
we do devil's sticking which is fair you
know devil sticking some more
complicated manipulation tasks
and these were the first projects we
worked on
and then it continued essentially in
1992 with a collaboration where we
started with a japanese research lab the
atr labs and the person in charge over
there is mitsukoto
who's been a prominent person in
computational motor control both with
biological and robotics interests
and there we started actually to work
with anthropomorphic robot arms which
were very expensive at this time
and he was capable of affording some of
these robots and i started to actually
spend a lot of time in japan and work
with these robotic systems
and this was then starting to be about
manipulation doing
complicated tasks with robots faster
catching balls
um dancing movements manipulating the
world
who did you study with in germany in
germany this was
a the department for
engineering design that was professor
klaus allen spiel
who's more known in the german
environment he's truly a
mechanical engineering design professor
focusing about
how to be creative in the design process
and
working with computer design systems and
things like that
and then who did you work with at mit
the prime person was chris atkinson
who was there as a professor as other
people in my environment included mark
rayburt who's well known for his hopping
robot
and emilio beatsie who is a very
established neuroscientist and
motor control of monkey neurophysiology
so so what were the some of the first
um did you take neural models and put
those into the robots
or how did you what we try to do is much
more understanding
the principles how the brain generates
motion and that is
okay so going back a little bit
in the late 70s it was a very prominent
vision researcher you might know
david maher who basically started to
try to put more structure in the way how
you address problems
in terms of talking about theory
algorithms and implementation
and i think what we've been doing in
motor control at mit
was primarily very interested in
algorithms and theory
of motor control and then basically
looking at
implementations in neuroscience as
something which would like to understand
that implementations in robots which we
would like to synthesize
and that was kind of the back and forth
but there's a branch of neuroscience
which is systems north
neuroscience that people really try to
understand well how does the brain deal
with the physics of the motor system
and how can it learn how can it
basically get better how can we do
imitation learning and
how can we really bootstrap autonomous
motor systems
out of some basic computational
principles
so what were some of the central
problems that
um would think what we were very
interested at this point which we still
are is actually how to bring learning
and autonomy into robotic systems
so there was not too much learning going
on in the 90s and it's still if you look
the field of robotics well has two
branches the one is mobile robotics the
one is what is called manipulator
robotics
i think in mobile robotics people have
been doing much more machine learning
techniques towards data filtering
sensing
and kind of state estimation in general
what people would be calling that
learning in manipulator robotics was
less is also more annoying because you
work with real-time systems in much more
stringent ways so a mobile robot can
stop and think
a walking robot cannot just stop and
turn off its motors just falls
over and so getting machine learning
techniques into these systems was really
something which you care about and
lots of the work we did focused
essentially about skill learning how
how can you learn a complete motor skill
from like throwing a basketball into
hoop
or doing devil sticking or grasping a
cup on the table
how can you learn that how can you teach
a robot how to do these things totally
by itself and also improve by itself at
the task
and what do you think the big
breakthroughs have been over the last
couple of decades
big breakthroughs faster computers
um besides that i think machine learning
has matured tremendously
in comparison to the late 80s early 90s
to what we have now it's now the field
of statistical learning machine learning
which used to be neural networks in its
original idea
and what we can do in terms of data
processing and kind of
empirical inference on on data is become
profoundly solid in good work
um this one component i think
getting into new ideas how to approach
robots
i think okay inappropriately
telephone um
i think the idea of imitation learning
and basically learning how to bias
robots was a very important component in
robotics
that you instead of just try to
pre-program everything from
human intelligence you show a robot
something and it can repeat that it's an
idea which is old has been around since
at least from the beginning of the 80s
but it found a very new wave i think at
the end of the 90s and has now become a
very prominent field in robotics again
another component which i think is
important
is to go away from robotics is plagued
by the idea that people always
reinvent how they approach a particular
motor skill or manipulation task they
come up with new representations they
come up
with new maybe adaptive or machine
learning control
algorithms but from one task to the
other
there's not a lot of similarities so you
start from scratch
and i think people at the end of the 90s
early 2000s started to work a lot on how
to create uniform representations and
one
key word here is kind of the idea of
motor primitives how can you find some
generic building block of motor control
which you can reuse
in as many scenarios as possible which
basically boils down to a representation
which has some parameters
which can be reused but which can also
be adapted to the new task in an
intelligent way
i think that was very useful um
model-based robotics which has been
around also for a long time
is a very important topic the idea of
getting away from high gain control as
originally in robotics
to what is called low gain control and
using internal models to get the
control accuracy this is i think
currently one of the most important
topics to get robots out in the real
world
if you want to collide with a robot when
it's out there in the world
it should give in it should not be stiff
like an industrial robot and hurt you
and in order to create this
controllability you need very good
models of the system which you often can
only get with machine learning
techniques if the system is
sufficiently complicated
what is some of the biggest problems
that you're facing right now
what is
if you if you really want to go and
bootstrap a robotic system out of
machine learning
we're still i think having not all the
tools we need to learn reliably i
personally care a lot about kind of
what's called black box learning
that you can have a learning system
where you feed in data and you're
guaranteed to get
a good and reasonable outcome that does
not work in grand general
in machine learning so far in particular
not in real time learning for robot
systems
so robotic systems when they learn have
this big problem that they have to
explore the environment
in order to gather data and then make
from this data better
get better ideas how to improve and that
is still a very complicated
process to guide this exploration how to
explore the robotic systems living
the systems i care about live in very
high dimensional spaces
that means just random guessing in those
spaces is totally impossible
and you need to really find out ways how
to explore the environment
how to extract useful information and
then
insert this into your control mechanisms
to get better
so i think this is remaining a big
problem
in general i think
we're still having a problem with
actuation and power supplies in robots
we are far away from humans and
it's a little bit handicapped by this
technology which is
something which hopefully will be
resolved in the future
and getting reliable and fully
autonomous perception into the system is
also something which has not really been
accomplished
in a sufficient way and then getting
everything together like
a complete perception action loop where
perception tells the robot what to do
and actions then tell the perception
system that it needs to do something
else otherwise
we don't perceive well enough or we
don't we haven't acted appropriately
and getting a complete autonomous system
going
is still quite a bit far down the road
so what was your phd project at mit
when i worked at mit was primarily about
using
machine learning techniques for control
it was not my phd project my phd project
was still in germany which was about
computer aided design and artificial
intelligence
mit was really about machine learning
for for
robot control and understanding
biological motor control with similar
tools
and where did you go after your phd
after my phd it basically became
primarily machine learning and robotics
and computational neuroscience basically
going between these three areas doing
behavioral experiments partially imaging
experiments with humans to study how
humans actually
accomplish certain motor behaviors
using data analysis techniques for
machine learning to look at biological
data
and using all those insights to put them
into new technology into robotic systems
so a lot of things which we've developed
in the last
15 years i would say are actually
motivated by biology like
there's a lot of imitation learning
which we've been doing representations
which are
useful for motor primitives and machine
learning techniques which
which mimic some component of what the
brain might be doing
which have become by now purely
technological approaches but they
got the ideas often from biological
research
and you went to georgia tech i went to
georgia tech for a short time
a bit after my postdoc mit i was a
research professor at georgia tech but
simultaneously i spent a huge amount of
time in japan so i didn't really spend a
lot of time with georgia tech at this
time
i was simultaneously also a research
professor at penn state university in
psychology
for behavioral experiments i did that
with a collaborator
but during i think the time of 1994 till
nine actually until 2002 i spent most
likely
four months a year at least in japan
what was the research institute in japan
atr
is a telecommunications research lab um
but it's divided into several branches
one branch was basically
the the department of midsole covato who
is
working on computational neuroscience
and basically computational approaches
neural network approaches learning
approaches to motor control
who's always had a very strong interest
in trying ideas
from computational neuroscience on real
robotic systems
so they were i think in 1996 we got a
very
big funding program kamado essentially
got this program which is called a
erato program it's like a science and
technology center for the national
science foundation
which gave us five years of generous
funding to basically
start working with humanoid robots to
scale up a little bit
i think in 1998 we had one of the first
humanoid robots
in the world i think vasada university
has all the robots and honda came
shortly afterwards out with their robot
but that was kind of the way for
humanoid robotics actually started to
gain attention
and they received a lot of funding from
the japanese government for that kind of
research or is it mostly
university or corporate funding no the
atr is funded by some japanese
basic research agencies similar to the
nsf so we were funded by this
japanese science and technology
corporation and that's basic research
not industrial money that was the key
funding we got to
when i got started my faculty job here
at ufc
it was essentially from the japanese
point of view that
i was still part of the japanese funding
system in this arato project
for them it was just like taking a group
inside of japan and putting it abroad
which is actually permissible within
this project and for usc it was just an
external grant
but it was only this one source of
funding which i've been getting and it
was always run through atr essentially
that i could get this money
when did you come to usc that was 1997.
and can you talk a bit about what the
robotics lab was like when you got here
and started
your research um let me see
who's here at this time i was
simultaneously hired with maya matarich
who you interviewed just a few days ago
and
there was primarily as people doing
robotics george becky
who basically worked on many different
branches of robotics he can
most likely talk about this much better
but he was interested in mobile robotics
he was introduced at some point in
grasping and manipulation he was also
interested in biomedical engineering
i think he founded the biomedical
engineering department here at usc at
some point
another person professor working here
was ari rikisha with that point started
to move towards nano robotics which was
a very new wave very aggressively
new idea in robotics and
gaurav sakartner was also shortly later
starting his career here and
moving from mobile robotics more into
sensor networks
so it was a nice but a small environment
in behalf here i think
in comparison to to about 12 years ago i
would assume
we at least doubled or more the the
robotics program here
do you do a lot of collaborative
projects with the other faculty here
we have some joint programs so with
god of the card maybe we actually have
currently a joint program on on grasping
and manipulation and and
particularly in a novel and unknown
environment it's about basically
manipulating objects and grasping
objects which you've never seen before
and the idea is that you learn to do
this in a in a test scenario
or in a training scenario i should say
and then later on in a test scenario you
get confronted with new objects that you
hopefully can generalize to those
the idea is to go from simple grasping
objects putting them somewhere
to extreme things like take putting your
hand to the gym bag fumbling around
figuring out what's the triangle
do you collaborate with michael rv
michael and ivy
collaborated initially a little bit on
topics of computational neuroscience
which were closer
so we talked about behavioral
experiments
we wrote some grants together but it was
never
actually some of the students worked
with me much more so we worked more
through students together rather than
other things which was about
doing simulation studies together and
then basically exploring certain aspects
of computational model control
particularly mutation learning is
something which michael threw interest
in mirror neurons was very interested as
well
you have other strong collaborations in
the us
um i've always had a very strong
collaboration with chris atkinson who's
now at cmu so it's just my former mentor
and we've had
basically never ending collaborations in
the end
um let's see other
some collaborators at mit occasionally i
have a fair amount of collaborations
abroad still which is
people who used to work with me at some
point like the epfl in switzerland
there's
people now at max planck institute in
germany
there's other people who are in
scotland actually to be precise which
makes black institute it's max planck
for biological cybernetics
in typing
and how many phd students have graduated
i think i created about six phd students
and i
usually have my lab on the order of
between four and six phd students and
then
two post docs or more so it's a moderate
size lab
under most of your former phd students
still working in
robotics and academia um
they're not all robotics people they are
machine learning people they're people
who are interested in computational
motor control
they are and where are they so
one two three four of them have faculty
positions
in different universities others are
postdocs for instance at cmu
or in canada others went to industry
actually the minority went to industrial
jobs
one of the things we're looking at is
the kind of mentorship networks and
collaborations
so what do you see as the central
professional organizations
for the kind of work that you're doing
in robotics
central professional organizations
conferences
well from the world of robotics it's a
relatively easy
game to play so it's ieee
international robotics a conference on
robotics and automation acra
then iris as the other comp big comp
conference in this area the new
conference which i helped co-founding
is robotic science and systems which has
been around now for five years
which is a smaller conference a little
bit
pushing reviewing standards and in
robotics and trying to
highlight kind of the coolest robotics
research in every year
i spend also a lot of time at machine
learning conferences like neural
information processing systems snips
and icmail and then lots of other
little conferences including actually
society of neuroscience meetings in your
control of movement that started kind of
primary meetings for
computational motor control and biology
and neuroscience
so uh what are the uh robots that you're
working on right now
okay lots of robots so here in the
background we still have some robots
this is so
these are old arms from sarcos which are
hydraulic robot arms one is a
master robot the other one behind me is
a slave robot we are interested in
ocular motor control and active vision
so we have heads we are getting actually
a complete walking humanoid in the next
few months from sarcos
which is a hydraulic robot it's just
similar to human appearance legs arms
heads fingers for grasping and
manipulation
we just a few months ago got
a pr2 from willow garage as a personal
robot
to work with we have some small scale
educational robots than our robots which
you might have seen from
aldebaran in france we
have some buried arms for
studying manipulation and grasping
particularly interested in tactile
manipulation and tactile perception
in the context of grasping and
manipulation
and then in about one and a half two
years well one and a half
years we will get a korean robot which
is the yubo robot which is a humanoid
robot about
i think one meter and 60 high which is
part of a national science foundation
major research instrumentation grant
i think that's pretty much all of them
it's a lot of robots actually these
things
and these dog robots oh the dog robot
yes that's an that's a
darpa project which ended just about a
year ago
which was about a machine learning
project how to use machine learning
techniques
needs machine learning techniques to
teach a robot how to walk over rough
terrain
what are um what are some of the crucial
problems facing um
grasping manipulation and tactile
there's a few things one is dexterity
that means having squishy fingers that
you can give in and
using actually a lot of force control
how to manipulate objects
tactile sensors are still relatively few
in hands
and there's no really super good
robotics hands out there so far
and learning how to basically now use
massive perception from tactile sensors
four
sensors at a hand together with
proprioception
receptive sensors in the arm and
use those signals to guide manipulation
to know when
manipulation is not just grasping
something and putting it somewhere it's
really
if it's not good can you reorient it if
you
notice it slips in your hand can you
re-grasp can you put it down
if you need additional information can
you just rotate it in front of your eyes
in order to get more information
i think working with a huge amount of
sensors and using them intelligently to
guide grasping and manipulation is a
very interesting topic and i think this
is partly what we've been working
starting to work on
in this one new project which we're
having
and have you been working on grasping
manipulation prior to this project
we've worked a little bit on that it's
part of our other robots they all have
hands
we've never had a hand with tactile
sensors so for the first time we've got
one of the new
buried arms which has a hand and they
have
tactile sensors on all the fingers and
the palm that will give us some
very new amount of information we also
have a collaboration with
a professor in biomedical engineering
jail lope
who developed some new tactile sensors
for the fingertips which are very
similar to human
skin sensors so it can they can give you
heat perception they can give you a
pressure perception
they can give you sheer perception they
have actually quite amazing properties
and we're just
starting to put them on a robot arm and
try to explore those
don't worry
and so have you also worked in walking
robotics you said you're getting a
full human we've started to work with
walking robots well with quadruped
walking robots on our little
dog robot and with biped
robots we've worked primarily in
simulation and with a humanoid robot in
japan at atr
whoever similar robot haven't done too
much walking it's much more balanced
control stepping control
doing basically full body manipulation
while standing on two
feet and balancing walking is
well the hardware we're going to get the
sarcosaur what is very special is a it's
a force or torque controlled robot
which is relatively rare in humanoid
robotics
and it will give us an ability to do
compliant control
in a walking robot which is not so much
explored so far
so we're getting essentially into it but
we're also approaching it maybe from a
slightly different angle than other
people have been doing before with
more traditional zmp controllers and
other walking mechanisms
and have you done work in human robot
interaction
as well we are interested in imitation
learning essentially which is a form of
human robot interaction
we are not doing a lot of work in terms
of studying how a robot should
interact with humans in order to be
appealing so
i think what we care about is to create
human-like motion which is our interest
in biology
kind of projected onto humanoid robots
and that's a component of human robot
interaction but it's not a
it's it's not a major focus on what we
are doing
so maya mata rich does that for instance
and i think she has a real focus on that
yeah now what is the best strategies
been so far for
for uh imitation learning
um
[Music]
i think there's many philosophies
we did some about eight years ago with
collaborators who are now at the epfl
in switzerland alca iceberg and
primarily
um we developed an approach to imitation
learning with motor primitives which is
a
kind of acute approach which uses
differential equations or dynamical
systems to
kind of capture imitation and then this
approach is able to reuse it and
generalize it to
to new motion to new scenarios
and i think this has left a little bit
of an impact in the
robotics community as many people tried
to do
either our approach or they kind of used
techniques which were inspired by this
approach so motor primitives and
imitation learning
has become i think something very
prominent
now there's a huge number of additional
projects which i would not be able to
enumerate without forgetting at least
three or four so i wouldn't try to do
that
but i think the idea of motor primitives
has become pretty central to imitation
learning as
one component and increasingly more
people do machine learning in
in this area which is also part of our
approach
and what have been the most fruitful um
biological and neurological inspirations
i think from our point of view it's
actually very related to what i just
said it's this
we created these motor primitives out of
the idea that dynamical systems
approaches are very interesting
to generate behavior where you can
guarantee that it will end up in an
attractive state which is in the end the
goal of the movement
and that you can also use them to couple
perceptual information into the systems
of instance if you're walking and you
see
some other person walking you could
synchronize with this person so that you
both walk the same fast
or if you're catching a ball you can
synchronize with the perception to catch
the ball at the right spot
these are in the language of psychology
still called perception
perception action coupling phenomena and
they're very beautifully modeled with
differential equations
and so all what we've been doing in this
regard actually was inspired by the
behavioral literature on this topic
which is
at least 20 30 years old to use a
central pattern generator
that is essentially the idea behind that
essential paragenerator which is an idea
which now goes at least 100 years back
that was originally explored in
invertebrates or lower vertebrates
and i think it was taken into more
human-like
behavior much later in it by the
psychologist essentially who tried to
model
behavior in general like in humans or
animals with
pattern generator like approaches and
how does that
play out in your research um
it works very well can you give an
example of
how you utilize those where we just
basically you
by now basically our motor primitives
are basically a form how you
represent periodic motion and how you
represent
kind of point-to-point reaching or
discrete motions
with the same representation and these
are all dynamical systems they have
either a point attract or they have
limit cycle attractors
and with one representation you can
literally capture
pretty much all behaviors which you
would like to generate so you don't have
to think about representations at all
anymore it always works what we're doing
and you chain them together you can
chain them together
you can just create one which is very
big depending what you want so you can
basically have
for instance a tennis forehand has one
big motor primitive
if you wanted to you could kind of um
partition it into first going backward
and going forward in our world this is
not really necessary
we can do it all but one and we can
actually now create really
relatively complicated behaviors out of
just one
behavioral representation what's the
hardest task that you've
successfully accomplished
i think the little dog walking over
the little dog robot walking over rough
terrain is most likely
among the most impressive complete
behaviors we've generated
since it's really it's rock climbing
it's going up staircases
it's going up or jumping over
little hurdles and things like that it's
it's quite a
complicated scenario and it's basically
all now running off
the same kind of software and the same
representations
it's more difficult than double stick
it's much more difficult than level
sticking actually devil's sticking is
surprisingly simple
which is why we do it for recreation
what's easy for robots is not always
easy
for humans and vice versa unfortunately
so level sticking is
from a technological point of view it's
what is called a regulator task you just
have to keep
an object at a certain repetitive
behavior
that means you just have to hit the one
side of the hand
in a certain position and then on the
other side of the hand in a certain
position
there's very simple kind of control
algorithms which can do this really well
and normal juggling is similar
normal juggling is actually similar
except that
people have studied juggling for a while
so three ball juggling is pretty
straightforward
the interesting part is the catching
with the hands and the coordination with
the hand and having actually compliant
hands so
if you have stiff robot hands and a ball
drops on it it just bounces off before
you catch it
so having the right kind of hands helps
tremendously when
you can replace it actually you can take
instead of the hand take a funnel
and that will work just beautiful take
bean bags which have a soft impact
and you can make a robot juggle
relatively easily
three-ball juggling is not so hard this
has been done actually chris atkinson
did this with one of the humanoid robots
in japan
um five ball juggling is harder because
the timing constraints of the accuracy
becomes harder
and if you want to go seven or nine you
just need a very fast and accurate robot
and then you would be able to do it as
well
you juggle yourself three balls
not very hard
um so what do you think the uh
what would be the the technology that
would
be the biggest breakthrough for the kind
of work you do
okay besides hardware i think power
and actuators are one
big missing component and a huge amount
of sensing on the robot is another
missing component so
biological systems has sensing and
abundance robotic systems are extremely
sparse
so there's hardware components i think
machine learning
using machine learning a massive amount
of machine learning and robotics and
learning how to autonomous really
bootstrap a robotic system from almost
birth so to speak to fully functional
behaviors would be
really really exciting and it's still
not very well done
lots of manual programming still in
robotics
were there any other roboticists or
books
or specific robots that really inspired
you or influenced your work over the
years
well there's the center attack textbooks
and robotics again i was not there
enumerating them because i would always
forget some
um what is the most inspirational
i think in neuroscience the idea of
mirror neurons most likely was kind of a
cool
cool finding in the mid 90s which got
people again thinking about imitation
very loose connection actually between
imitation and the mirror iron system
but that was kind of a it's not a book
it's just
research papers i think they were kind
of inspiring
in general i think the progress of
machine learning is is really
among the most fascinating to make
progress in autonomous systems in
general and try to understand how to put
these things into
robotic systems big difference between
learning and robotic systems and what's
normally done in machine learning is
that
most machine learning algorithms get a
bag of data and they just analyze it
robotic systems generate the data
themselves and have to live in an online
real-time scenario usually we can
generate data
in abundance it's not an issue how much
data you can generate it's much more how
can you how can you generate intelligent
data where you learn something from
so there's a lot of work to be done to
learn in perception action loops
which is learning control in general
reinforcement learning is one
special instantiation of that
yeah otherwise i don't know any
particular
component which is the most inspiring
it's lots of bits and pieces which come
together
um
where have you gotten most of your
funding i think
most of my fun well let's go through
history i think
initially most of my funding came from
the japanese science and technology
corporation together
with atr in japan and then afterwards
primarily national science foundation
and then there's a little bit of nasa
and recently a fair amount of darker
funding which is
there's some projects which relate to
machine learning on robotics like
the little dog walking project or now
the
new project we have about grasping and
manipulation
i think the the bulk of funding usually
was national science foundation
do you remember how you first got
interested in machine learning
i think i was generically excited about
neural networks when they basically
gained popularity in the late 80s
i was interested in self-organization
and i was interested in learning machine
learning and then
just especially neural networks were
kind of the cool thing to do at this
point
self-organization complex systems theory
was i think another very cool thing to
do at the late of late 80s early 90s
and trying to understand how to bring
these two things together
that you can at some point think about
machine learning algorithms as being
completely self-organizing
which has actually been realized to some
extent
not entirely in in the current work of
what people have been doing
was kind of fascinating so what was the
first work and self-organizing systems
that you
looked at well i went to this santa fe
complex system summer school where you
just get exposed to
the staff examples of complex systems
like diffusion equations
self-organization in fireflies i think
steven strogatz
at mit was very popular at this time
with his work and showing how
just animals can self-organize into
coordinated behavior without really
directly communicating
just analyzing dynamical systems attract
the dynamics trying to use attractive
systems to generate coordinated behavior
like pattern generators do was one part
of that
when did you go to santa fe 1992
a long time ago
okay um is anybody else that was there
when you were there
who was giving lectures at that point
there was a lot of excitement at this
point i think about the upcoming wave of
synchrony in the brain
which is wolf singer and people who
worked on this topic these are german
people at max black institute
that was i think one component
low dimensional dynamical systems and
analyzing them
was very popular no just a whole bunch
of lectures on that nobody
in particular and beginning of machine
learning and neural networks are
obviously becoming more and more popular
but i don't recall anybody who should be
mentioned over the others it was just a
great
series of lectures essentially and i
don't recall everybody anymore at this
point yeah yeah
um and um what have you been the um
some of the big breakthroughs and vision
systems or some of the big challenges
you faced in vision
also yeah we the thing is we don't do
too much vision ourselves this is just
another
discipline and we usually just try to
use vision we're very interested in
activision ocular motor control that
means how to move
eyes around in order to gather
information in order to get better
perception and then
basically do better better movement
afterwards
um yeah i'm not an expert in this area
to really make particular claims
i like currently the development of
basically increasingly more
depth sensors from the very beginning so
rangefinders or cameras which actually
give you a complete
depth image of the environment that
helps a lot to do
unlike what most vision people are doing
starting to do kind of point cloud
reasoning which is a newer topic
i think the mobile robotics people have
been doing this for a while since they
work with rangefinders for a longer time
and now i think in big robots just
becoming more popular as well
okay is there anything else you'd like
to add about no
i'm happy i assume you will edit this
anyway
yeah okay
all right well done well it was fun