if we can start with your name and where
you were born and when
okay so my name is raja shatida
i was born on january 29
1952 in damascus
syria and
and so then where did you go to school
and how did you
kind of get to college and decide where
to go for college
yes so i went to school in damascus
actually i went to a school which was
french and arabic school it was a school
run by the french
french organization uh close to the
french government actually
in syria because syria during
the period between the world war one two
uh was under french mandate so there was
some relationship with france
and cultural relationships but of course
at the time i was born syria was
independent
so i went to to this school i basically
it was a school where you go
to uh primary school
and then through high school it was the
same place
um and
in i was always attracted by
science and math and so on
so um the um
my math professor actually
when i was 15 16
uh was a french guy
and since i was one of the best students
in
the school he offered me
to obtain a grant from the french
government
to go for higher studies in france
and well of course i accepted because
that was
a unique chance and therefore i left
syria when i was 17 after graduating
from high school
to go for higher studies
uh so in france uh of course
the system is always more complex than
as elsewhere
uh we have a system uh which is called
um
engineering schools or complicated
hires which is
living side by side with the
universities it's not within
the universities and
usually i mean the tradition in france
is that people who are
very good in mathematics and physics and
so on
preferably go to those schools
but entering those schools requires
first to go to
a special preparatory
school during two years
for it's called higher mathematics and
special mathematics
uh and then you have a
competition actually it's uh it's you
take an exam it's a competition
and according to your uh
your marks or the the the school to
which you applied and if you succeeded
you go to this engineering school so
this is what i've done i'm sorry
it wasn't the classical
university however at the same time i
also took some some math courses at the
university
of paris it was in paris and that that
school was called seth louis
it's in the latin quarter in paris then
i went to an engineering school
in the city of tourist in the southwest
of france
it's the school national school of civil
aviation
so there i agreed
three years later as an engineer in
electronics
specializing of course in electronics
related to
to aviation more than general
electronics but it's it's it was a
general
diploma in in electronics after that
i made an additional
diploma a kind of additional master's
degree
the engineering school is three years
after the first two years
in controlled science and
my dream was always to to do science
research and
not to go to industry that's
i wanted to contribute to science so
and and initially my
hobby or my attraction was to
astronomy and astrophysics although i
went to an engineering school finally
it's
not necessarily that you do engineering
if you go to those engineering schools
in france you can do anything afterwards
even finance for example they are very
i would say you have a general education
and you can do almost anything
anyway so uh after that i went to
apply i applied for a grant at a
research
lab nearby in toulouse for
[Music]
doing a phd
in in
space science and astrophysics
and that first you have
also an additional master's
degree to pass so this is where i
started
and then
[Music]
something happened uh actually my
my advisor
passed away at a time and
[Music]
the lab next door was
las laboratory for
systems analysis and architecture
which had a different name and data
it was more close to control science and
i had met
also someone who is george gerald
uh and therefore
i decided to go and
do my thesis on robotics
in that lab so why robotics
during my my studies in the uh the first
masters in control science
uh in controlled science i had also
a training
and project on machine learning
so i had already some knowledge
about robotics and some interest about
the mystery of
studying artificial intelligence and so
on
so it was something that attracted me
also but
it wasn't as strong as my
dream when i was young about astronomy
and astrophysics however
faith decided that i do this
in a way and so i started the thesis
with
george and
that was in 1977.
so uh george
was george giraud was actually
he's an exceptional person
uh was he was just back from the united
states after a sabbatical
at berkeley and
there he was completely captivated by
robotics research that was going on
in the bay area and
[Music]
specifically about studying
mobile robots mobile robots as opposed
to
fixed robots which are arms and at that
time
the main study and the main application
of robotics arms was manufacturing
mobile robots on the other side was
a different issue it was about making
a machine move in its environment
discover its environment
etc which was a really big question no
one
really knew how to do that so he wanted
to start this project at last and
so it was a good coincidence because he
didn't have
a a student to
work on this project so he hired me to
do that and hence
i was it appears i mean
some a person doing uh
you know research on on science
not the scientific uncertainty but how
science scientific research
history told me that according to his
own research i'm the first to have
got a pitch degree on mobile robotics in
france
and i got my phd degree in
1981. so that project
was called in french hilar
which is the acronym of the robot on
which i wrote
and basically the
[Applause]
issue the study was about robot
navigation how
to make a robot a machine a physical
machine
move in its environment
avoiding obstacles discovering the
obstacles which you didn't know about
in the beginning taking decisions to
avoid them and
and going to a given location
so that was my physicist subject which
was called
robot mitigation space
modeling and decisional processes
and yeah and of course
this today is is quite common i mean you
see modern robots moving all
over the place but at that time no one
knew how to do that
so it was uh quite a challenge and
one of the i'm happy to be one of the
first to
to have started on this so what were
some of the problems you were
working on as part of making the robot
move around
and explored space
well there were i would say different
kinds of problems
uh and and it's important to
to say that since the beginning
i had to consider them all and this is
very important in robotics
to have this systems system approach
system view
because everything is interlinked so to
start with
uh it was a real world it's not
it wasn't a simulation and by the way at
that time simulation computer
simulations
was something not as easy today
i mean i'm speaking of times where
microprocessors
were just starting to be used we had
mainframe computers
so it's it's not and we didn't have
uh computers with
a an operating system that enabled you
to have a good
user interface mouse
all this didn't exist so
to get back to the challenges to the
issues that i was working on
it was the first challenge the first
issue is that
it was a real robot that i had to make
move
it wasn't a point on screen
which means you have all the uh
difficulties of real experimentation
uh and all the hustles of real
experimentations and
and the problems that come from that
uh the fact that the comforting power
wasn't
so important as today uh so we had
a system
uh which was with very
low computing power on the robot itself
it was the early
8080 intel 8080 microprocessor
and so
since it wasn't possible to do all the
competition on the robot
it was radioing through a very very slow
radio rank
9600 baht that's very slow
uh and to
a small mainframe computer
which itself wasn't sufficient neither
which was linked to
a computer in a
national computing center which was in
paris so
i was in the lab in toulouse
and this big made from computer which
was in ibm 360
was shared uh with other people of
course doing something else
so you imagine that making experiments
wasn't
that easy and something that you decide
on the spot
uh so that's from a practical point of
view
actually and also there was building the
robot of course i wasn't by myself there
were
other people working and
some engineers and researchers
to to physically build that robot
and integrate it and so
also there was the sensors
that we were going to put on that robot
at those
early times we were investigating the
use of ultra
ultrasonic sensors sonars and those
weren't just manufactured
like that you can buy them you have to
make them yourself
so we made the sensors
and we wanted to have vision on
on the robot so uh
vision is a very at that time it was
of course a very important issue in an
open problem
and um another phd student came up
came in to the to the lab also
afterwards
maybe a year after me to study
vision on the road and
in the meanwhile because we are naive
and optimistic
uh we said well by uh
maybe we'll we will leave this guy do
his research on
purely on vision he'll be ready in three
years but in the mean
and then vision will be solved
uh but in the meanwhile we need to do
experiments with other sensors so we
also
bought one of the first laser
rangefinders
which of course wasn't absolutely used
for robotics it was used for
topography things like that it was just
a pointless
laser like the laser pointers you have
and so we mounted it
on a motor to scan the environment
very slowly at that time also so
i mean the experimentation was also to
build the robot and the experimental
conditions
uh were not of course uh as easy as
today
so that's a first set of situation where
you had to
to do the physical mounting of the robot
again i wasn't alone but that was one
issue
the computer infrastructure the program
for communication
and so on and then there was the
scientific problem that i was addressing
which was robot navigation
so question how
do you represent space objects
the robot environment and it was
important to understand that
for making the robot move it was
mostly important to represent free space
so that the the structure
the the structure that the robot was
going to use
to compute its motion was the free space
structure which
had to be derived computed from its
perception
of the obstacle layout
so how to structure free space and
it was one of the issues to understand
that we needed geometrical
representations and
we needed topological representations
which expressed the way space
portions are connected so one of my
contribution was to find a way to
decompose free space
into regions geometrical regions
and build a connectivity graph of those
regions
and there was
already at that time i said we need also
a third layer of representation which
was semantics
which was not just the shape of the
areas
not just just the way they are connected
but also
some
[Applause]
something that designated them in terms
of
uh usefulness or
dangerousness and so on uh or any other
label
but that wasn't so much used at that
time
basically we focused on geometry and
topology and then
the second question is okay once
you have this free space
representation instructor how to move
how to compute the motions
of the robot in this space and this is a
motion planning problem
which wasn't formulated at that time
so one of the of the problems was to
express how you move an object
in a space spatial
representation and other peoples were
working on that other
like thomas lozano perez at mit for
example
which has written a very
similar paper in 1981 actually
we were working on the same issues at
the same time but i
had graded it before
that and i had also my own approach and
represent representation of robot and
so the issue was to reduce the
complexity
of this motion planning uh
by correct presentations of space
and the robot so that was the the this
condition
uh and of course it meant not
just to devise theoretical models
but also to implement this on the
computer
as a computer program uh and to make
this
run of course debugging the program and
so on and so on
with the tools that we had at that time
and
then make the real experiments and make
the robot move
so that what it was about mostly
what were some of the specifics of your
approach compared to the approach
other people were taking at the time
um well one um
i think one uh important
uh point was um
i want to represent uh
space uh as much
as possible close to reality
so for example uh
today it's well known you can have
different kinds of representations you
can have a grid
regular grid superimposed on the space
and basically the space is digitized
into this grid
which of course makes you make some
approximations
of the shape of obstacles and so on and
if you want to
have good approximations reduce the
[Music]
secretization induced by this grid you
have to have a very
fine grid which means
a grid that has small
cells which means a high complexity of
computation
which was completely uh
to avoid at that time
so my approach was to say let's try to
represent space in a way that is as much
as possible
without deforming the shape so i came up
with a
model which relied on the geometry of
objects and
acquired by and therefore
free space was represented actually by
polygons
i came up with the networking for
decomposing free spacing
into polygons it was 2d um
those polygons represented the exact
uh free space shape and
it wasn't a grid it was an actual
representation of free space and there
were polygons so that
those polygons were convex
that's one other issue so that in
each cell in each convex
cell the robot can move in a straight
line
from one frontier to the other and so on
from one cell to the other
the polygonal shape
was in a way an approximation if you had
for example
some rounded
parts of the of the environment but
mostly at that time were working
indoors so you had walls uh
objects that are always more or less
original in shape so it wasn't so
bad in terms of approximation however
the issue was to represent space as much
as possible
close to reality so that was one point
in this
facial representation
and the other point was read to the
motion planning issue itself
so there were several
approaches already known about
planning motions and the idea of
representing the
environment using those polygons was to
extract
a graph representation which is the
topological representation
and then to compute robot the motion of
the robot using
this graph so extracting geometry into
the topological uh structure and then
doing a graph search
and there were some some uh
techniques for them we have searched
like the a-star algorithm that was
uh around since 1972
and the um
the the the graph that was uh
that i built uh in a way
was something close to
uh two techniques that were around one
was
is called the visibility graph which is
a graph that
represents the shortest
shortest path in free space
and the other it's called voronoi graph
which is
a graph that represents uh
outside middle points little ways
between obstacles so that the robot is
not
going to hit the obstacles so our my
representation was closer
to the visibility graph but i had to
take into account of course that the
robot had a shape
which
made it of course not
uh go into the i mean taking
the shape to prevent it to go into the
obstacles
so uh the configuration space as
expressed by thomas at that time was
the idea which was very
very important was to reduce robot
shape into a point but making the
obstacles
larger by half of the weapon so i didn't
take
this orientation i i made the
computation
in the geometrical space and not in the
configuration space
so that was another difference
and what were some of the studies that
you did experiments that you did with
this robot and
the navigation so of course
it wasn't enough just to come up with uh
those methods they had to work actually
on the machine
right uh so
i made colleagues at that time
sort of experiments for making the robot
move
from one place to another we set up
an experimental environment with wooden
obstacles
and so on that
[Music]
we made so that we can
give them different shapes and
that were light enough so that
the robot collided it wasn't
uh broken the robot was really
in in aluminium and steel and it was it
was really
difficult to uh to to break the world
and indeed i remember on one experiment
there was a colleague working
on the table and the robot
made a mistake and so the robot at full
speed
went and collided with the table
on which that colleague was working
and the robot was so strong that it
moved the table
that that guy was somewhat afraid anyway
so we had to make real experiments and
real experiment mean a lot of failures
and what was
interesting is to study the causes of
failures
because when you have a failure in this
context what you do you get back to your
programs you
make some debugging and so on
because maybe the fear is due to that
competition or something
and then you get back to experimentation
and you find out that some failures
don't disappear
so what's happening there
you could go around those failures by
approximating things for example because
robert was
executing its path and
[Music]
after moving a few meters
uh it started to go into the obstacles
to hit the obstacles
instead of avoiding them so you could
say well let's
make some approximation and artificially
augment the size of the robot
but you could also say hey what's going
on why is it
always doing this systematically
so asking this question
is i think the sense of
um uh using the scientific method which
means
you had a kind of theory your program
your system
your models uh that should
uh have enabled the robot to go from
one point to the other over the gospels
but it wasn't doing that
so which means
[Music]
your
something was wrong something wasn't
taken into account in the model
so uh it was
a kind of discovery uh to
but that was after my phases so i'm kind
of
[Music]
jumping in time but i'll get back to
the to what i did uh
so it was that question that led
to the discovery of a phenomenon
which was uh that there are
when the robot a phenomenon simple i
mean today when you think of it it's
quite simple but of course
when the robot moves well
it's a machine moving it has wheels it's
moving
on floor therefore
there is some slippage
when we compute the path when the robot
computes the path that it's going to
follow
there are some parameters to take into
account
like the wheel diameter
the distance between the wheels and so
on all those parameters
are uncertain
but in at that time the computation they
were considered as being
exact um
there we didn't consider that moving
uh was also generating uncertainties
on robot position so we had to think
over and say well there
are problems that you cannot go around
with that you have to really cope with
it's the fact that the rot is generating
uncertainties
while it moves and by the way
how did we get obstacle positions
we can we got them by perception by
using this laser range finder
but the laser rangefinder provides
measurements
which means they are also
uncertain we considered that those
measurements were exact
in the beginning so necessarily
we have to take into account to consider
that we have uncertainties on the robot
motion we have uncertainties on
perception
and this should be uh
treated as a problem okay
that was in 1984
so in the meanwhile after i
got my phd in which those issues were
not considered
so we had made it successful experiments
and so on but
in
seeing those issues without really
considering them as
you know as important but then i
after my phd i went to
stanford university for postdoc
so i stayed there for a year
with tom with tom winford
at stanford airline
and working
on a project tom binford wanted also
at that time to have a mobile robot at
stanford university
so there was a precursor a few years ago
earlier with hans moravik who had worked
on
stereo vision at stanford university and
at that time hands has moved to carnegie
mellon
and tom biddeford wanted to start a
project with a new robot
in the project with
the company is now adept but at that
time
it was uh animation
and so brian
carlile was heading at that time
the company and they've built a robot
which was
omnidirectional it was able to move in
any direction
it has special wheels called the swedish
wheels
that enabled to do that so um
basically during my postdoc my job was
to
procure this robot from animation and to
set it up and to implement
my thesis results on it so that was
what i started to do what i've done
during my postdoc at sanford university
and then i came back to france
in 93 in
1983 and i had a
position at cineres which is the french
national center for scientific research
as a researcher and i continued my work
as a researcher
[Music]
when did you get to so stanford was in
stanford
basically in the year 82
to 84. now 283. it was one year
okay and who was there at the time who
did you
meet or work with so basically
the professor the leading professor was
tom winford
but there was
also there usativ so i'll get back to
osama because
we had met before uh some academy there
was uh
harlan baker for example uh
where this group that said there was
john craig
there was ken salisbury so very nice
people
and very competent people that was a
great experience for me to
to do this postdoc stay at stanford and
the whole scientific environment of the
university of course
unique you meet people
like also bernie roth
so i wasn't working with him but of
course bernie is
also a unique character in the
robotics community and this is where i
met him first
and the environment around
stanford university in the bay area
there was sri
at sri i've met people
like will stillson
so this nelson was for me a kind of
reference because he was
the father of uh shaky
and sheiki the first paper on
a mobile robot was published in 1969
and there was a reference for me during
my thesis i mean
that was uh really a marvelous project
because
first it was a really pioneering project
and it since the beginning they had the
inside
and the understanding to consider
all issues from
higher planning decision making to
execution
system architecture and so on i'll get
back to the smart picture as well
but so that that was really a reference
because
the seminal paper really
identified the all the issues
and solved them in a way of course that
was enabled by the technology of that
time
but many many
important ideas were invented during
checking
strips a planner a star
[Music]
the use of visibility graphs
in
for guiding motion
so it was it was really a
great project so also meeting
these people you know it's like
it was really wonderful uh and also i
met people at
berkeley there had been a
uh robot project also at berkeley
which inspired george but after that
they
didn't really uh develop that
so people at birth were more ai and
control people
so um and and what we said it was
berkeley also at that time
[Applause]
so names
so and then you went back to cnrs as a
researcher
that's right so that was uh in in
1983
so then i in a way i continued to
to work on on the issue of robot
navigation and really
considering those problems that i
didn't completely address during my
thesis now it was
issues about uncertainties and
another issue was about
going further in representing the
the environment as i said there was
the geometry the topology but semantics
semantics in terms of this is
a room this is a corridor this is a door
and this is how to extract those
information
how to discover the structure of the
environment
in terms of its semantics of its
structure
with those labels
from the basic geometrical structure so
as i said i had
a basic structure which was the cell
and the graph and
in work with jean-paul and john paul
had joined the lab at that time
his thesis was to to use those
representations to try to organize the
graph
and from the graph extract the semantic
information so he was working on that
that was his thesis and i was working
with him also on that
so several topics and
then as i said
there was this issue of uncertainties
which was important
and there was also another issue about
architecture
so i'll get back to uncertainties but
architecture
is basically the question of the
organization of the robot system
since we have programs
doing reception programs doing
space modeling programs doing
motion planning programs doing motion
execution
all those program programs had to
communicate with each other
in a timely fashion
so that when we issue the command to the
robot
to go someplace it actually does
all those computation and it was there
and we wanted to add also
something which is beyond just using
a plan to move it's also detecting
new obstacles like people moving and
avoiding them
and continuing to reach the final
destination so
there was also an issue about using
obstacle avoidance
algorithm we came up with
and there were other work on this issue
also of course
by other people and
so to take into account also real-time
situations like detecting obstacles and
so on
so this method let us think about
how to organize robot control system so
that
it can cope with those situations
of being at the same time
able to plan execute motion and at the
same time
to take into account unpredicted events
uh real-time uh constraints and do that
so uh and this uh
future issue was uh also not so much
addressed at that time and
i was one of the directions that i was
working on
there was also another colleague working
on that
as part of his thesis but i was very
much
in this so different directions
who was the colleague who was doing this
the thesis
uh his name is he became
a research engineer at class afterwards
so
those different directions were actually
investigated at the same time
and the
to get back to the issue of
uncertainties uh
when uh
we considered that it was a problem that
we should
really address and cope with
then we started to do some theoretical
modeling of
that and
we understood something which was
really tricky
it's that
we needed to build a map of the
environment that was what the robot was
doing
to uh
[Music]
to to move and
we needed at the same time to do
to build this map incrementally as the
robot moves
in its environment to be able to
build this map incrementally
we needed to
put together different views
and how do you put together those
different views you need to try
to to to anchor them in
some location which was the robot
observation location
and to connect those different views
connecting those different views was
using
robot different robot positions but
different robot positions were
themselves uncertain so
and the way the robot localized
this itself it's by using its own motion
it's on the metric measuring
displacement
and this as i said generates
uncertainties
so the only way for the robot to
really
localize itself and correct
those uncertainties related to its
motion
was by observing its environment
okay but that means that it was
correcting its position using the map
that it was building
and as i said to build this map it
needed
these different positions to
put the map pieces of the different
views together
so mapping putting those views together
and localization which is correcting
robot position using
the map itself were two problems that
were linked
it wasn't formulated this way in 1985
8485
it was more formulated as
how to map the environment
in the most exact way taking into
account
uncertainties but by asking the question
how to map the environment
in the most exact way concerning those
uncertainties
you discover that you had a chicken and
egg
problem with correcting robot position
using the environment and using a robot
position to build the map of the
environment
this is how slam came up as i said at
that time
it wasn't called slam and one of the
first papers about uh about
sram again with a different name and it
was more
uh space modeling uh uncertain space
modelling and so on
uh was a paper i've written with
jean-paul in 1985
you had done this research in 1984
already but it was presented at the acro
in 1985 at southwest and that was
one of the first people actually dealing
with with islam
without using the word slam
[Applause]
uh i'm getting into many things because
there are a lot of
connections uh i wanted to say something
also uh when i was in the united states
actually on my way to stanford i stopped
by boston
and mit and this is where i met
people like thomas perez
whom i knew
before because he came also to lose
before that
so i didn't met him for the first time
in boston but
before but it was also
very important for me to
to see him again and rodney brooks
rodney brooks who has finished just
finished his
thesis at stanford and moved
from stanford and at that time was at
mit
i think in the meanwhile we want to save
uh for
some period that at that time he was at
mit
and so with rod
brooks it was uh
in my view at least a very
uh how would i say
war contact i mean i felt really
as like i've met the friend and so ralph
is is someone uh i assure you very much
is
really also an exceptional i suppose you
had
an interview with him or you are going
to have one
so his is an exceptional person
and but at that time he was working
on also
similar issues motion i'm assuming about
82
on emotional plan applying actually his
thesis work which was about
using generalized cylinders for
object recognition planes basically
on motion planning using so we
went into some discussions and
uh i've met rod many many times
afterwards
of course and the interesting thing that
he was
doing work in similar things at the same
time
so he also in the
[Music]
1984 85 was working on
uncertainties and he wrote the paper
also on this issue of
uh modeling space using uncertainty
taking underground insecurities so it
was very interesting and
rob came to to lose also at that time we
had a
[Music]
conference and
rod afterwards worked also on
robot control architecture and he came
up with a
well-known
subsumption architecture at the same
time i was working on a different
approach
which wasn't subsumption because i
didn't believe in that
[Music]
for robot architecture based on several
layers
including planning and refund control
and so on
but he put plenty of way
so yeah that was i mean those
years
[Music]
80 84 85
86 were very rich years
where several people were working in
different directions
um with in a way
i'm going to make the conversion but
it's not
uh of course it's
just a comparison uh in that on those
times uh
robotics teresa research in in this
area i mean modern robots and so on
uh made us look to
different problems so each individual
working in mobile robotics so taking
example rodney brooks myself uh
other people was interested in looking
into
motion planning and to space or
presentations
into robot architecture
[Music]
how to organize the system so
nowadays people are much more
specialized you have a slam guy you have
the
motion planning guy you have the
uncertainty processing mdp guy and so on
so the comparison is with the
renaissance
as compared to uh today's
scientist you know of course i said it
dance very much and
it's difficult to
be competent in all domains but
when it started people were competent so
i think this sculpture of having a
systems
view and and looking to all
aspects is very important to understand
that everything is connected you you
cannot solve
a perception problem on a robot
without considering that this perception
is not made by just for itself
it's made to be used it's made for
motion for action so there's a
connection between perception and action
and there's this connection has to be
at the core of the way you process
uh the sensor data
it put constraints on sensory data and
the action is the connection between the
robot and its environment
so you have to also take into account at
the same time
the continuum perception action and
the interaction with the environment and
of course this
constraints on the decision-making
processes which have to be
able to cope with real-time constraints
unexpected events but at the same time
enable the robot to take decisions at
the long term
to anticipate and so on so you
if you don't have this you miss the
global picture
and therefore the solutions that you are
going to come up with
won't be uh
the right ones they won't give you
the possibility really to have an
integrated systems
working together
[Applause]
so then after at some point you also
became interested in
in learning right um
or more deeply in learning well yes
actually
this is interesting because i started
with
a project before my thesis it was during
as i said my master's in control
uh i had a project in machine learning
it wasn't robot learning uh it was
on machine learning so i worked somewhat
on that
and then this was just a
kind of a problem still open problem
uh that i didn't address for uh for some
time
then it would it was addressed again
when i worked with jean-paul on his
thesis about
learning environment structures but that
was a different kind of learning
it was actually structuring the data
that the robot had already already the
representations
that it had it was more
a way of extracting information
from data structures
but i didn't get back to learning
uh in terms of
so that was in the in the 80s also i
didn't get back to learning
as as such
until many years later
in actually
the years 2000
to
with with a different work
which was more related to
trying to learn almost from scratch
the
sensory motor representations that are
related to
uh representing
objects and actions so the idea that so
we are jumping
uh a long period of time uh we are
jumping along period uh in in in that
in those years i had student who was
very very excited about that
william
actually is in san francisco a small
company
in san francisco so he was
very much excited about the issue
of learning
representations from scratch what does
this mean
it means that
the robot initially has
very very little knowledge about
everything about its environment about
itself
it has just uh simple
processings for example in an image
it would just extract gradients and
edges special directions and so on
very simple stuff in terms of its
actions
uh it all only knows about uh
controlling for example if it's a wheel
mob robot
controlling each wheel separately just
making small motions consider that if it
has it was
an arm it has an arm it was just moving
fingers like that without
controlling the whole
action so the challenge was
how to i mean consider that
a baby just born
just knows how to move his limbs
or her limbs and doesn't know
and just perceive vaguely some shapes
but no conviction between those so the
idea was how to make those connections
so that was another issue that i've
worked on
with williams and another student who
came up
afterwards in colorado
on developing a system that would learn
[Music]
sensory motor representation as i said
the representations
of space object and so on are going to
be linked
to what the robot is able to do with
them so that
it develops its representations
at the same time that it develops its
own actions
it learns to control its motion by
and through developing more
sophisticated representations of its
space
and this is guided by a learning
process uh with some motivations some
values some value functions uh that
that start with very simple
values and also themselves become more
complex
as the robot knows to do more complex
things
and you mentioned kind of the human
maybe is
you know something that learns in this
fashion of testing the world
did you have any theories
of that sort in mind while you were kind
of coming up with this robotic learning
mechanism yes of course i mean uh
again we are speaking about the years
2002
2005 and so on and in the meanwhile
uh there has been a wealth of work
in robotics inspired by
biological systems neuroscience
and so on at the end neuroscience
researchers were coming up also with a
lot of theories
about how we develop our abilities how
we learn
and so there was
there is today a very rich
interaction between those areas
neuroscience and robotics
so there's inspiration of course because
the only working system that we know
about
is biological
human beings or or animals uh
but my view is that it doesn't
necessarily have to be that way
it's not uh
in a way you can say in my opinion
uh we have an instance of
intelligence of the way of perceiving
interacting and so on
uh an instance in uh biological
systems human beings but it's not
necessarily
the only instance of achieving
intelligent systems or systems able to
interact with their environments to
learn and so on if there is
a general theory or theories
of uh intelligence and learning and so
on
they this general theory could be
applied to different
instantiations different ways
of doing it so basically we do it by
using our neural net which is
our brain we do it
by
having a mechanism for learning which is
encoded into the way the neurons
interact together and integrate
information and of course the studies
from neuroscience
and the mathematical models of neurons
explain to us how this works but at the
same time
when we do artificial neural nets for
example this is implemented on computers
on turing machines on
a general model for computation
so is the brain amenable to
a turing machine that's of course
an open question and
if yes
it's it means that uh
there is indeed a more general model of
computation that you could use
and not necessarily using just
uh neural systems to learn you have also
other
theories for learning other methods for
learning
which are based on mathematical models
for example you take reinforcement
learning
which is uh based on optimization
processes
at the end of your tuition
because they are using finally build
better equation and
and not integrating uh no
signals so uh i think
i i believe i mean this is my view
uh we can achieve uh
artificial intelligence with robots
not necessarily imitating
biological systems but we have to
really seek this general
theory of intelligence that can be
implemented in different
different ways uh
natural intelligence evolved with a lot
of constraints
that have been taken into account by
evolution uh
and and we can probably
build systems that have maybe a
different approach
but at the end which are as intelligent
as
as natural systems so that was my my
view
hence i'm not advocating i didn't do it
with my students neither
to imitate exactly what's happening or
the theories that come from neuroscience
they were a source of inspiration and a
proof
of concept in a way that you can do that
but you don't need to really
implement it the way it's done in nature
all the most that at the given moment in
time
we have only models of what's happening
in the brain
approximate models faults models more
controversial models
proposed by neuroscientists and so on so
of course we could implement those
models to help neuroscientists to
[Music]
maybe uh prove that this model is
working and this one is not more working
computationally speaking
but it doesn't make sense to have
a kind of religion which is
the only way to go is to imitate nature
i don't believe that
it's there are many ways to go and
nature is one
of them a great one a source
of inspiration and the proof of concept
again but
i think we have to seek the more general
theory in a way and i think
one of the um one of the things that i
skipped over was your eaton project
oh yes uh thank you i wanted to before
you answer the question can we uh
i think we the microphone got turned off
oh oh since swim oh just a minute or two
ago
yes indeed so i have to repeat something
no no no no i got it here i just hit
that
okay go ahead and try yeah it's okay
good
[Music]
this is why i'm here because it's like
um yeah that way i can pay attention to
you
instead of wondering whether stuff is
working or not
we've lost big awesome and it's or not
from here but i've been passed and it's
yeah
yeah you know you go through all the
effort you lose one itself
anyways so okay eden project sorry
yes so the eden project actually
uh in the meanwhile uh
in as i said i worked on this issue of
uncertainties
in 1985 but then
came several projects
that more or less oriented my research
so one uh
issue one problem was related to
for example using mobile robots in
warehouses
it was funded by ibm france
so we built a small mobile robot
smaller than the mission one and
worked on that for doing some
uh indoors navigation warehouses and
automated docking of the robot
and and so on based on the research i've
done before
and the women also control architecture
so
each time we had projects they were
a funded project they were addressed but
at the same time they served
as a basis
or as a means to develop
more profound or more general scientific
questions
not just do the project
as an engineering project but as a
scientific project
so i had students working on that one of
my students from kristen race was
working on this issue
and at the same time working on the
issue of
architecture because uh you have to to
cope with that
um so then
came the time in the mid 80s
of european projects that was
a great thing because uh
uh the
structure was that
each european nation was doing
research and we met as scientist
as colleague in international
conferences
we almost never worked together in
europe
and the european projects were those
projects funded by
europe by the european union at that
time it wasn't the agreement it was the
common market or
eec european community
with the objective of making uh
different european nations working
together on
joint projects and there was this
project
called skins uh
with different european participants
there was germany france united kingdom
greece
uh and
[Music]
the the problem was about
fusing sensory data having different
sensors cameras fixed or mobile
mobile robots observing
a situation and trying to understand the
situation by fusing all these data
at that time the
our partner from the united kingdom was
the university of oxford
mike brady who is another
[Applause]
exceptional character and
he had someone working with him and that
guy was huger and white
so this is uh this time of
hugh had had uh actually um
had his thesis uh in uh
united states with uh rosanna bakshi at
the university at japan
and uh i've met you the first time
at rosettas she was giving a party
uh in 1985 when when when i came for
this
conference at centralists with the paper
on
[Music]
and you have read my paper and during
the party he discussed with me about
the important out they point out pointed
out that there was
a mistake at some point he was right
but it's not it wasn't important but
whatever
so and this is where i met you
and he was also someone i appreciate
very much
a real friend
so we worked together with the mike
brady's group and
hugh specifically on this
project and this project addressed the
issue of
fusing testosterone data which was
exactly the problem
that i've started to
to to address with the
[Music]
processing of uncertainties and so on
but in a different context
but that was also a very exciting period
uh in 1985 i think the project
started in 1986
for four years and
so i met a few different times at that
in that period
and also
another project came up in at that time
it was called it was a european project
as well a different kind of european
project
called uh autonomous smoker robots
amr and here
for the first time the issue was to
study
outdoors robots robots for intervention
outdoors so
and there was some industry involved in
this project
french industry for example metro
was involved
and i'm
going through projects and time very
quickly
and just a second can i ask you
something
um
yes he's going to appear at some point
but i had said 5 30
but if he i'm not sure he wanted to
appear earlier but i'm not sure what
happened in the end
so if you take these um
and hang out there since i think we're
okay
you can keep an eye out
i had to change this in 20 minutes or
okay 15 minutes but i don't know how
long i'm gonna go okay we should
probably be okay by then
yep so sorry i'm sending you you
can take some food too okay
there you go thank you okay so we went
to amr
yes and then so and the in
19 so it's working
and in 1989
if i remember there was
a big excitement about planetary
exploration
mars
and the french
national space center class
which which is our nasa uh
asked us to come up
with a project for mars exploration so
what they did
was a very interesting thing they made
a kind of national project and they
asked different
research labs to come together
uh to study all the aspects
related to uh sending a robot
mob robot on mars uh asking about
the robotic side of it of course uh and
george geralt
was the coordinator of this project so
he was
again very instrumental in
making things well coordinated
and and advancing things
so we had many partners from
from france working on different aspects
and we had the responsibility basically
in my lab a class
of navigation over
rough terrain and
uh the other aspect was
about control architecture and
general planning while other worked on
locomotion
wheels or other systems tracks
other worked on vision specifically
other
work on communication and
the interaction with the ground station
and so on so
at that time also we studied how to uh
have a remo a kind of tiny operation but
we called it tether robotics which means
the
system has the remote robot because of
the time necessary for communication has
to be
autonomous enough to do
its tasks and to not put itself in
danger
uh uh
taking into account that you cannot just
still operate it
with like 15 or 20 minutes delay
between earth and mars and mars so
we also developed structure
architectures
for having this disconnected uh
systems where you had landing system on
earth
and a supervisory and
execution system on mars to
achieve this capacity because of course
sending a robot to mars means robots
that doesn't have much
computing power so this is why the
planning was
i mean i was you know desk planning and
we wanted to
have the motion planning and navigation
issues on board the oil
so uh starting in 1989 we had this
project
and what
we did what i did is that to organize
work internally in my lab
using uh for selling all this
with the external partners
i've started a project called eden
we had now i'm seeing about 1992
there was an event
in that pyramid which was
the collapse of the soviet union and
the opening actually of the soviet union
of russia
to the western world
and the actual russian
the russians were very much advanced in
developing
robots and structures and machines
able to move on
harsh terrain one of the robots that
they developed
was used actually turnover in 1986
to remove debris and rubble
and just after the the collapse of the
soviet union there were
uh the french some french
industry as you know like metra from
atom
uh had uh boats
from russia one model of those robots
and this robot was called
and hence the eden project of course had
to
use this robot for testing
our work on mobile
robot navigation and natural training
so it means actually this it's a
experiment experimental displacement
uh in natural environment
in french and the robot was adam
that that we used on that
and so that was the early
80s uh 90s sorry 93
and so on uh that we really developed
a full working system with
novel techniques novel algorithms novel
approaches for perceiving and modeling
natural terrain and
planning motion on natural terrain
taking into account
uh the fact that on natural terrain you
have
slippage you have stability problems you
have to
actually cope with different kinds of
situation you have sometimes
flat terrain sometimes uh slopes
sometimes very uh uneven terrain
and and powertic terrain all this has to
be
modeled taking into account devising
a us algorithm for navigation in this
case
so that was a great adventure uh
of coping with a novel issue and very
exciting one because
the purpose was to go to mars
and remember i was
my my initial excitement about astronomy
and so on so
it was a dream come true
to to work on that and
so i think that was really successful
because we came up with really
seminal results on that and
of course others were working on that in
the united states mainly
at jpl at cmu
and we had uh many exchanges
and and meetings and discussions in
international conferences and so on uh
with the uh uh
the the people and same young people at
chippewa it wasn't joint project
[Music]
itself but of course scientists meeting
that was very exciting
unfortunately in a way in 1994-95
the french
decided to stop this project
because uh the european space agency at
that time
put the emphasis
on earth observation and not
on planetary exploration
and so the money went to
developing first a european
uh shuttle which was called hermes
which never was really developed
and then to programs on
earth observation which finally ended up
in the
international space station itself
instead of winter exploration
and in the united states the
project of mars exploration has had
continued
and in the former soviet union also
and i will get back to that in a second
and finally we had
a social murder on mars in 1997
96 so that was uh
i think a great thing in the meanwhile
as i said
uh the russians were also continuing and
another robot was bought from russia
by another french company called alcatel
it was the martial hot model which which
has
conical wheels and which has an
articulated body
a very agile robot and we had it at the
lab we worked with that
robot it was called llama because it was
able to
really move anywhere and
on that robot we had also it was part of
the eden
experiment and we were able to do a lot
of
interesting things on again
agile motion planning on uneven terrain
and
coping with sandy
or gravel dunes and so on
and another model was both also and was
used on a
experimental terrain at the space agency
and tools the french decisions in crews
and that one was called eve so we had
adam
and eve uh at the same time
uh yeah so those were very exciting
years and
uh and then kind of disappointing
because we never
went to mars but we were ready we had
everything
running it wasn't uh space ready
but in terms of the results the
uh systems uh
[Music]
it we were really ready
so yeah so who were some of the people
who worked with you on this i'm just
gonna
check so in the uh
eden project and more generally on the
project of
mars exploration at that time i had
a student who then became a researcher
at class
simone lacroix who is
who started working also on skits by the
way on the
skits project i mentioned uh
well as as i said george geralt was the
coordinator of the project
uh also rasheed alami was working on
that
he was more working on the issues
related to
planning and and
control architecture and i worked a lot
with rashid at me
on those issues afterwards at that time
and afterwards
and that in the in the late
80s early 90s we developed a control
architecture
journal enough for mobile robotics which
is
which was used all of our robots
and that was also an important work in
the area
and also another person
nick simeon who was the
motion planning guy i would say only
terrain he came up with
a very clever algorithm for taking into
account
the terrain configuration and he has
made
very very uh
good work on that because we were able
to experiment really on
uneven terrain now nick is working on
other topics in motion planning as well
it's
on molecular motion motion
studying how molecules deform how we can
combine proteins for example
this is also one of the great
application of robotic motion planning
into biology is working
so other projects yes another project i
would like to mention
was uh the comets
project that was in 2003
4 5 so it was about
uh aero robotics drones
uh it was a european project with spain
mostly
and portugal
and at that time we became interested
in robots that fly
and so don't forget that i've
made my engineering studies in this
aviation school
and my specificity my
master's in control in
the engineering school which is called
national aeronautics and space school
so i had also this
tropism to aviation in general and
it was so it was very interesting for me
to move into
iowa robotics and that project was
very interesting it was about uh
cooperation and cooperation between
different
flying robots uh germany uh university
of berlin was also a partner
the idea is to have different
robots we had blend helicopter so on
cooperating together of course
controlled
and cooperating together autonomously
to for some task and the task was
detecting
forest fires and we made a lot of
experiments in portugal also
the team went there and simulacral was
one of the most active in this project
to
[Music]
build the the understanding and the
control system
for the the
both the control of individual
robots and our case we had this flame
and
which was called karma and the uh
other the coordination with the other
flying robots and there are some
projects continuing now
on this on these issues coordinating
with ground troubles
robots and so on but that was one of the
first
projects actually in aero robotics
that we have done and this is also eu
funded
it's eu funded yeah eu funding was
really very very
important in stillways and they seem to
do very large
projects yes uh it depends
i mean uh it depends because there were
times where
we had small ones then they grew up
in terms of funding and then europe
is really now putting
very much the emphasis on having
both smaller projects like two three
partners three partners
uh working on a three year project
not really small but and large
integrated projects
with more partners or maybe longer
and in the future what is planned
is what
we call flagship projects 10 years
with enormous amount of funding
but many people of course participate in
that so it's
yeah i think this will push research
and european integration much more
another project i want to mention maybe
this will be the last one i mentioned
but it's a very important one
uh it's the communion project so
uh in the uh in that time i mean uh
asking about two 202 203
the one of the branches at the european
union which is called future and
emerging technologies
one launched a an initiative called
beyond robotics the idea was to
study robots with advanced cognitive
capacities
advanced interactive capacities
and so on and i
have coordinated the project
called cognora which run between 204 and
208
with partners uh from europe like
hendrik christensen
who was at that time in sweden uh
christian dopamine in in england
rudiger dillman in germany
and many others
covener means the cognitive
robot companion so the objective of the
project was to study cognitive functions
uh of of a companion robot
what is a companion robot it's a robot
that interacts with people
assist people uh
just do tasks for people and this was
a very important project to study the
issues related to human robot
interactions
how to model humans from the perspective
of the robot
how to jointly do tasks with humans
but also on the more
robotics aspect of cognition which are
autonomous decision making planning
cognitive architecture how the system is
organized
learning learning was one important
issues
there and this is where i i mentioned
what we have done and that
with some other partners working on
other aspects of learning for example
obia
in the pfl working on invitation
learning
was in this project as well uh
royal seaguard was in this project
working on
other issues related to perception and
navigation so uh cogniran was
really a project that put together
many competent researchers from europe
working on those
issues about learning decision making
cognitive architectures human robot
interaction
and communication dialogue which was the
university of budapest
and that was an integrated project a
large project
and i think that it was really very
seminal and
yielded a lot of results that are now
exploited in other projects
european or national projects and so on
do you have a feeling for why
there was a such an interest in
developing a companion
robot well
yes um i think
what happened
over the years if you wish was that
in the beginning we were in the
beginning of robotics
50 years ago mostly the
motivation for robotics was
manufacturing
was automated manufacturing so
people people wanted robots in
the factory mostly
i would say at that time to replace
people to
make work faster
more productive more precise
and so on and so on so it was robots
without people when we studied
which was a different thing at all when
we studied mobile robots and so on the
emphasis was on autonomy we wanted to
study
autonomous robots how to achieve
uh how to build a machine able to take
decision by itself to move by itself
to avoid obstacles by itself to go
places by itself to do things by itself
without any human intervention the the
human intervention was
just in the beginning to give a goal
and when we started mars rover of course
it was again autonomous robots on mars
no people and again the emphasis was
on autonomy some 3d operation which
means
there is a person a specialist
doing some controlled remote control but
very light control but the robot is
physically influenced
as those autonomous functions were
developed
at the same time
came up some issues in society and
basically in the industrial world it's
the aging society where
people started to address the problem of
what what is going to happen when
everyone is going to get
older so
we needed some more maybe some
assistance or some
help from machines
so the issues came up and mostly i think
japan was very uh
driving on this to study robots for
assisting humans
so in a way there was some maturity
in understanding some basic
autonomous functions which are necessary
for robots to interact efficiently with
humans
without the humans controlling every
emotion and so on
that was a necessary step
and there was a kind of need i don't
believe it was
an expressed need and it is still not
the next question
but there is i would say an anticipation
of the fact that
we are going to need robots in our homes
uh so this convergence
of this potential need
and the capacity to achieve
enough autonomous functions so that the
interaction with the humans
could be easier for humans
started this
issue of robot companions i think
and made it possible
and there was a potential
needs so it was the situation was mature
to study that
and and i believe it's very fastly
developing
and indeed we have robots
with more or less
enhanced capacities for interacting with
humans for example
a vacuum cleaner like roomba doesn't
interact with humans but it's in our
homes
and we have some experimental devices or
very very simple interaction
but this is probably a great avenue to
explore
to achieve robots that really are going
to share our space
for our benefit of course so yeah
what do you think are some of the
challenges for robotics in the future in
the next
i don't know 10 years or something
um well
i believe uh the
main challenges of intelligent machines
are not solved today i mean the
questions that
we try to address
in the early days of robotics are still
open
general perception scene understanding
situation understanding
this is not solvent it's there are
results there is progress but
i don't know of any system that you can
just put anywhere and
it understands understands meaning
analyzes
a scene says well this is a camera this
is a chair
this is a door and
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these are two people speaking together
must be an interview well like
yeah so this doesn't exist
and this is a very fundamental problem
perceiving and understanding what you
perceive
so we probably
since the problem was addressed since so
many years
and we didn't solve it it probably means
we don't have the
proper instruments the proper theories
or the proper
processing power of the proper
algorithm and so on it won't be solved
just by
chance by augmenting processing power
you need
to know how to do it so
i think this is a very important
question and a fundamental one
but i want to say that it's not
to be addressed by itself again
you don't perceive just for perceiving
you perceive
for action and therefore
we have to consider the connection
between perception and action
all the time not just perception by
itself
the which means
you have to integrate perception with
action and this
will necessarily be based on learning
processes
because you cannot
imagine beforehand those interactions
between perception and action
without i mean abstractly
speaking so learning is another issue
to that that has to be developed
learning methods
learning theories we have a lot of
results in learning
but we have big problems with efficiency
with ability to cope with
real situation real problems
dimensionality
not toy problems so there is a lot of
work to be done there
as well i think okay uh and
to
of course there is the issue of human
robot interaction social interaction
but this is more or less
this social intelligence issue is
important i think it would be
instrumental in
accelerating learning of course because
you don't learn just
again by scratch by observing you learn
also by interacting with people and
learning from
people learning from
people for a robot or learning from
other robots
that's also this interaction is
important
what i want to say in terms of a
novel issue to address i believe that
a we didn't solve those issues and we
should continue and there are
mathematical issues there are modeling
issues there are
uh deep questions about how to project
to to to theorize those things
but what i believe is that we need to
if we consider all those things together
there is a point which was not so much
addressed
until now which is
the problem of self-awareness
or consciousness
what enables actually natural
systems also to
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drive or control or
achieve those processes perception
action interaction and so on
is related to the fact that
i have some awareness of myself
with respect to the rest of the world
myself is my body and
my way of
of of uh integrating
i would say the external world with
respect to my own
existence my awareness about where i am
so this is just to say that i believe
that studying
self-awareness for a machine robot
consciousness
we don't have theories of that of course
they will have theories of consciousness
for humans and so on uh but we don't
have a theory
a mathematical theory or a computational
theory of that
i think this is probably at the center
of those issues of enabling us to really
understand
and achieve a
you know an artificial intelligence
a human level of entrance
and just to wrap up unfortunately
um since i think maybe they're out there
i don't know
um could you tell us if you have some
advice for young people who are
interested in
getting into robotics well
i think robotics is a very exciting
field because when you
work in robotics you work in all the
other fields
at the same time because
basically in a way robotics is
about
developing an artificial being
so you have of course the issue of
sensing you have the issue of perception
you have the issue of
computation you have the issue of
building the body which means mechanics
material science you have
the issue of studying
the notion of mind which is
also related to philosophy you have
ethics
you have interaction so really entering
into robotics
is a wide spectrum
of possible studies in in all domains
all domains in conjunction
with the science that's going on in
those domains for example
independently from robotics so it's also
a multidisciplinary
venue i mean you you can discuss with
neuroscientists
with social science people
with philosopher they have
interests it's i mean i don't know about
any domain which is richer
than robotics it's it's really a
comprehensive
domain of course one will specialize
but that what's important is to keep in
mind
the full picture and
second point is that when addressing a
problem
we my advice is to
never try to solve it by
hacks of course hacks
will work sometime they will do
great things but those are hacks which
means they won't be
general solutions and so on and
doing this is missing real problems
again remember
the issue of uncertainties and how slam
came up
uh this was because we didn't do hacks
to solve the problem
we really tried to address it and we
came up with solutions
and which are were developed afterwards
and slam became
a great domain to understand robot
navigation
so no hacks you can use hex
if you want but you should know these
are hacks
and just for temporarily maybe
solving another problem but relax
great thank you very much thank you