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So
the stock will be on the experimental
rover project and we had some problems,
but here are the slides.
So I'm going to start with a little
introduction and tell you why
this project might eventually become
interesting and why we are doing it
and then talk us separate
into two main parts.
And the first part I am going to talk
about walking in simulation.
That is we take a physics engine,
build a model of the rover and try
to get it to walk in the
simplified physics with a few simplifying
assumptions.
And then in the second part of talk,
Felix will tell you about how we might
eventually move from simulation to
reality.
So this project is about
building a life sized humanoid robot.
And for the next
year at least, we are going to focus on
the legs. So arms might come eventually,
but only much later.
There are, as was mentioned already,
quite a few projects that are already
working on this, but none of them are
really a fully open.
So we try to make a fully free
project, which is both open source
software and hardware.
Also, we try to completely document
the development process.
The goal will be to have on state of the
art software as fast as possible, because
software, of course, is easy to copy.
And for the hardware, we try to focus
on something that is manufactured
with moderate resources.
Well, why would one want to have a
humanoid robot at all, and
we are, of course, in
dedicated environment, so if you are on
the street, you're not going to be the
wheeled robot no matter what you do.
On the other hand, human environment such
as this room are really limited
if you are on wheels.
So, for example, there are stairs over.
There are there are stairs you couldn't
get on the stage without
climbing stairs.
So there are many reasons why Wheelz
might not be so ideal.
If service robots eventually do
become commonplace, these environments
may change.
But at least if everything goes wrong and
there is a disaster, you
really need to have some alternative to
Wheelz.
But of course, I mean, for us, the real
reason is that we saw these videos from
Boston Dynamics and you
all saw them and you thought, that's
really cool.
It's unfortunate that the military is
doing it.
Let's try to do something
similar ourselves.
Well, it seems that the progress
on humanoid robots after several decades
is finally heating up and on the other
hand, the big company players like Boston
Dynamics and Traffic, which are now both
owned by Google, are really secretive.
So they published YouTube videos.
Make you that excited?
That I really exciting, but don't really
tell you anything and seem to be no
scientific papers or anything.
There are also university projects
that do publish papers, but
still they don't they don't actually
publish usually sars-cov-2, they
don't publish the drawings
and they are not really intended for you
to copy their hardware.
They are scientists and they write papers
and that's about it.
And the other problem is, of course, that
existing robots cost on the order of
several hundred thousand dollars, which
is completely unaffordable for hobbyists
now a dream at the moment, just to get
this down to maybe a few thousand euros,
which is still a lot, but
possibly affordable for a small group
of people.
Hackerspace also.
I would also like to mention that
this first part of the talk where we do a
physics based character animation, that's
actually a big topic in computer
animation, that they don't seem to focus
on putting this idea some real on real
robots to use physics as a tool
to get get
realistic animation.
So what are you going to do in a first
talk?
The idea is to take a simplified physics
model and try
to simulate a robot and then to develop
a controller that will keep it from
falling down.
The idea is, despite
actually working on the controller
to actually inform design choices for the
later projecto dissimulation will
ideally tell us what kind of maltose we
need, how fast they have to be, what kind
of years we need, how
well we can cope with Ansun
uncertainty and so on.
We are going to use a dedicated dynamic's
toolkit that we brought ourselves
from algorithms that are published,
but we also use the open dynamics engine
that was already briefly mentioned in the
last talk just to make sure that we
don't exploit any implementation
backs in our own engine to our
advantage that we have an external engine
that kind of lets us know that
this works with our, you know, the
assumptions we might unconsciously
make.
So how do you let a robot at this point,
and this is really the basics of all the
game physics engines that you may know,
such as the bullet and so on,
and the center concepts that of a
rigid body, which is a physical
body that is assumed to be completely
unreformable. So it can't bend.
It can flex.
And as a result of this, the massive
situation is completely convinced of 10
parameters and this thing has six
degrees of freedom, and except for
the ten parameters, the simulation engine
doesn't really care about how the mass is
distributed.
It is only natural for collision
detection, but not for actually for
actual dynamics.
The next step up in realism would be
the so-called soft body
shown here, a picture from Wikipedia,
probably most commonplace example of a
flexing beam.
Then suddenly you need the complete
details of mass distribution and so on,
which essentially means you have to make
a character or you have to design
your robot. You have to decide what
materials to use and so on.
And even then, this thing has infinitely
many degrees of freedom.
It's become a lot after democratization.
So this is numerically much more
expensive to simulate.
The method would be called the so-called
finite element method.
If you can and
you don't want to do this at the moment,
our robot to simulate it as a collection
of rigid bodies,
this is the model we use at the moment.
It consists of six rigid bodies,
the torso, which is just one piece
and two legs.
You have six degrees of freedom.
Peleg three in the hip,
one in the knee and two in the ankle.
Four six degrees of freedom, total polec.
And the advantage of this is that the
body that is the foot also
has six degrees of freedom.
So in this country duration, we can
essentially control the position and the
orientation of both feet,
but not more.
At the moment, so this is sufficient
for what we want to do.
Now, let's come to control our design.
Why is this even a hard problem?
I mean, if you've seen 3-D animated
films, you might think that this will be
easy, at least reasonably easy.
He is an industrial robot
and a biped, and there's really a lot of
work on industrial robots, we last talked
about this.
What's the main difference?
Well, the main difference, as you see
here, is that the industrial robot is
bolted to the ground with rather large
bolts.
And it is obviously impossible for a
biped because it can't screw
its foot on the ground every time it
wants to take a step.
That's impossible.
And this is what this is
what makes this problem hard.
The thing is that for the industrial
robot to control all degrees of freedom,
this is called fully actuated
and this means that any trajectory can be
followed.
This, of course, not completely true for
industrial robots. You then start to
worry about things like collision
avoidance and so on.
But if I mean, if the
problem were as simple as for walking
robot, then collision avoidance is
usually not a big problem if you're on
even ground.
So it would be trivial or this would seem
to be trivial, but it's actually made
complicated by the fact that you have to
just stand for it, which is not fixed to
the ground, and therefore
the intrinsic dynamics begin to matter.
It actually matters how fast you execute
a direct trajectory and so on,
and you really can't take any trajectory
any longer.
And I'm going to show you a little demo
for this.
Which is a thing here,
though, this is what we would what we
might like to get
is a model of a robot with no
physics engine and we might want
to get this.
It just walks.
I mean, just doesn't look particularly.
Well, that seems OK.
So now if you take this and connect
an natural physics engine.
Then we see something disappointing.
Namely, that.
It just falls over.
The thing is, the trajectory tracking
actually works, so you can see
that at the joint angles
in the robot, they actually more or less
tracked the trajectories they
had in the last simulation.
But the big problem is that the foot
which we cannot control, just loses
contact with the ground.
And this is exactly the kind of problem
you need to worry about if you want to
design a controller for a walking
robot, see it just fight over.
So let's do a little
bit of physics here and talk
about contact forces.
Now, contact is in principle a
complicated microscopic phenomenon.
But there's one thing that's actually
quite simple.
Namely, that context is usually non
sticky.
And I illustrate this as this cartoon
of picking up a box of
a place, a box or something
and have a box.
But this pullover will do that places on
the table and it's actually put towards
towards the center of the earth by
gravity. And the reaction force from the
table just compensates for
the resulting force of zero.
So by Newton, it doesn't move.
If I try to lift it up, I don't quite
succeed then.
Well, the lifting force compensates
a bit of gravity and the rest is
compensated by
by the reaction force
or contact force and the resulting
from the station.
So this is a so-called constrained force
that just takes the value it needs to
stop this from accelerating.
But finally, if I pull up what was
a large lifting force, then
the contact force would have to be
negative in this picture.
And this this would happen if it were
glued to the table.
But if it's just the normal contact and
it can't and it would just actually
accelerate upwards and pick it up,
it is really the central concept that
happened to the poor robot in the last
simulation.
Now, let's consider multiple contact
points.
Of course. I mean, this is,
again, a cartoon, and in reality, you
could have very, very many contact
points. But let's suppose we have four
and this is supposed to be a cartoon of
the foot.
Now, at each contact
point, there is a
contact force acting
and we can now define the so-called
center of pressure as the weighted
average of all the contact points.
And we weight them just by their
contribution to the normal component of
the contact force of
X2. This, if all the contact
force come from the next to an X,
C is going to move closer to X two and so
on.
We can write this with these weighting
factors, alpha, and
then the fact that the normal that these
events are larger than zero implies
that the others have to live between zero
and one.
Mathematically, this is quite a convex
some.
But even if you don't know what that is,
it's really quite obvious that if we take
weighted average of these four points
with weighting factors between zero and
one, then the average can move outside of
the box.
That's really all there is to it,
at least for the case of a box.
Now, this seems to depend on the
microscopic details, and that would make
it completely useless because we can't
hope to model this.
We don't actually know the microscopic
structure of the ground.
But there is a magic trick in a way,
and that is if you sum all
the contact forces into total contact
force and a total contact talk,
and there is this equation, phoniness in
this state, let us calculate
the center of pressure just from the
total contact force and the total contact
talk.
This is really nice because
we can now look at walking and usually
the stands of stationary.
So in this case, the contact forces
have to compensate the reaction force to
some robot body.
And then we can just calculate the
contact for the reaction forces
from the robot body and get
necessary conditions so that distance
for it a chance at remaining stationary.
And these conditions are well, first of
all, the center of pressure has to remain
inside the foot.
And second of all, it can't just
pull up at Stanford like so
for the total, total, normal
component has to be positive as much
as the sign
turns out that these conditions are
sufficient for those lift constraints.
So if you don't wish you can slip on
ground like this, then is that even
sufficient?
And usually this is sufficient in
practice. If you fail, you fail in the
way you saw in the last demo.
And now you can actually say
quantitatively you've either lost them or
fails because this is the center of
pressure that would be required.
And you see, if it does this kind of
work, it would require feet that are
huge, almost 70 centimeters.
This is not only unrealistic, this
can't work.
That would collide.
So this lets you know what really
happens, of course, is at the center of
pressure goes to the dotted line and then
stays there via the foot, starts
to rotate.
So this can't work.
So our new control strategy should focus
on contact forces.
Here's an idea. Imagine the robot
floating in space and you know, from
school it totally now and angular
momentum are conserved.
And this implies that your center of
gravity trajectory cannot be influenced.
John, as an astronaut in space and you're
floating away from your space station
and there is nothing that you can do
about it because the center of
gravity or center of Mars in this case
is moving away.
And no matter what you do with your arms,
you can change things
on ground.
Fortunate that you can do something about
it, but you can only do something about
it through contact forces.
That is obviously a relay or
not. Obviously, that is the relationship
between linear, angular momentum change
on the one hand and the contact forces
transmitted through the foot or the feet
on the other side.
So we make a simplifying
restriction and we assume that
a total angular momentum of the robot is
just zero.
The simplifies the equation
and then we're just left with the center
of mass trajectory and we can completely
determine the contact forces from the
center of mass victory.
In particular, a joint Ango trajectories
only matter insofar as they influence the
center of gravity trajectory.
We still have six contact forces, that is
six forces, three forces
and three Turks, and
we have six equations, namely that
the angular momentum has not changed.
We have the center of pressure and we
have the height of the center of mass.
Then you can solve a so-called boundary
of your problem to find a center of mass
checkley. This idea is from a
PhD thesis from Munich
Illustrated here.
So we assume that the center of Mars is
just a constant right.
Now we specify a center
of pressure. This is the blue line.
We specify this so that it changes
inside a legal region.
And then solving the boundary of your
problem gives us the green curve.
And if you track this center of
Mars or center of gravity,
then it will generate
these contact forces which are, as we
chose them to be legal.
Now, there's one slight problem, though,
because we would usually have three
boundary conditions, but only a second
order differential equation,
because Adelfa are usually
we are already walking.
So we have the center of mass position
and velocity at the beginning.
And we would also like to specify the
center of mass position
at the end so that it doesn't go
completely off
and we fix this.
So we get the third degree of freedom,
but just allowing our chosen
center of pressure trajectory to be
slightly modified.
The problem now is that the center
of pressure constrained by then become
violated.
What this is really saying is that
sometimes you need to sidestep.
So if you're walking in, someone pushes
you. You can't just keep placing
your feet like you want them to.
You have to take a sidestep.
And this is really what?
This mathematical strain to tell you
here,
however, usually so if you're not pushed,
for example, this approach works quite
well in the next
in the last step, we just take our
robot with its 12 joints, basic use
of freedom and use so-called inverse
dynamics to just control the contact
forces. And by this track, the center
of gravity.
Now, two cases, just four completely.
The next life will be a demo.
Don't worry.
So if there's only one leg on the ground
and we have six contact forces, again,
six, three, four, three
talks, and we also
have to swing like just on the air and we
have to test six generalized
accelerations.
Again, three translation, three
translation. As for your.
And each case will if you have two legs
on the ground, then you have to contact
forces, one for each leg.
In each case, you have 12 equations for
12 joints, degrees of freedom.
Now, this approach is
actually implemented here,
and I just need to slightly
play with my computer sci.
So this is the
first demo.
And this is now actually a physics
simulation with contact enabled,
so this, as far as the physics engine
is concerned, is physically possible.
There are still some assumptions, but at
least the contact is actually
is actually respected on the order
of hundred Newton meters, which is really
a lot. And this is
going to be the main problem really in
the realization talk.
But we also need quite a lot of power.
So we need on the order of two hundred
watts per joint.
This will be a topic in the second part.
So let's just summarize what we've just
seen.
And we've tried to design a confort
for a controlled strategy based
on contact force management.
The performance is quite OK.
I mean, there are better results in
simulation, but it's a start.
One thing is that the foot positions for
this controller actually fixed in
advance.
So this could be used by a higher level
controller to make it walk upstairs,
for example.
But it also limits the options we have
for push recovery.
And the idea to take a smart sidestep
is just not implemented in this kind
of controller, although it is simplifying
this friction at a total angular momentum
has to be zero because of this weird
Tozzo motion that you've seen in the
video and could try to fix
this by adding arms and make this
stereotypical walking style to keep
the angular momentum zero.
But at the moment, it doesn't do
so much harm. So we just could just leave
it like this.
So regarding this
last dynamic's part, we could, of
course, just take the physics engine and
treat this as a it's a black box
and then use black box function
and version to get our inverse dynamics.
But it is highly inefficient and also
numerically, it's a nightmare.
So what we do is we take this nice, very
nice book and look at some dynamics,
algorithms that are actually intended for
use in robotics.
And we implement them
and well, in a small
library that will be released
pretty soon now together
with the controller, so that everyone can
play this one
nice feature of the of the legs of our
robot is that they are analytical,
inverse kinematics.
So you can actually calculate without
using iterative methods
for joint angles, you need to position
the foot in a certain way.
This is just a nice feature that somewhat
simplifies the controller.
Now, in the longer term,
this kind of hand traffic controllers
that we had are OK for walking.
But if you're really if you push really
hard of the two and get really rough
or if you want to do acrobatics or
whatever, then it's
going to then is kind of simplifying
assumptions eventually break down.
And there is actually a large body
of resolve that people just use
large scale simulation.
So basically, you tell the roll to the
computer, this is your starting status,
your goal state, get me there
and decide this.
This uses this works offline
quite well. It doesn't work so well if
you have real time constraints, because,
I mean, if you push, you can actually
stop to think, of course.
So there are many interesting results and
eventually we are going to
look into that as well.
But as the goal
for now is to just walk on even ground,
well, it's probably
easier to stay with these handcrafted
approaches, not least because they're
easier to debark.
You can look at it and check
if your assumptions are still valid and
if everything works.
So that's it for my side.
I summarize this
crowded site again, and
these are now the requirements that Felix
will have to deal with.
Yes.
Yes, now it showed you
the simulation gives us
requirements for
yeah, for talk, so
our freak talk is in the order
of hundred meters, which is quite large.
It gives us velocity
requirements.
So about 20
radians per second and, uh,
a power requirement, 250
watts. So if you do the math,
um, that won't match.
That is because the peak velocity and
the peak torque won't show up at
the same time.
So on the outside,
the mainstream option there is brushless
motors
because we cannot afford industrial grade
motors. We go
to our favorite, our sea side
Cheapside shiny stuff
and uh, found this two kilowatts
brushless motor for thirty
dollars. It is quite heavy
but manageable.
Five grams but
overpowered but slow, uh,
to 70.
Cavey Cave stands for uh
yeah. Basically how
fast the motor would turn in
idle per volt.
Uh if you do the math again.
Eight hundred fifty irradiance
at thirty volts and uh
approximately uh three
Knewton meter at
ninety amps.
Uh so that gives us
a gear ratio of one to 50.
Um we get
to the gas later on because
uh, if you have a brushless
motor from the RCA
environment, you usually power your plane
with it so you can control
direction and maybe speed with the
usual affordable brushless controllers,
but not position.
And we want position control here.
So we implemented our
own, uh, brushless controller,
uh, which is encoder based.
So there is a sensor on the
rotation axis and
it uses a thing called space vector
modulation, which is like this
light says basically three
face AC face locked
to the motor rotation.
Uh, think of it like microstamping
for Stepa Motors.
And uh, as we are
switching, uh, high courant's, they're
nighttime's we are using,
um, four eighty
five.
Uh, because, um, yeah, it's
differential.
The power stage is
actually from a cheap
Chinese pressure controller.
They were kind enough to put us
on it to.
Mm. Uh, pitch
but had us and you can just
unplug it from the ship
as I elapse microcontroller
and use a SDM thirty two
uh and build
a nice new baseboard for it.
And uh if it ever breaks
or burns just pay twenty
bucks, get a new one.
Uh and hope they don't change the design.
So as
we are trying to, to uh
uh to control the talk
and the talk is proportional to the
current, uh, we need to measure
the current of all faiths or
at least two phases because you can
calculate the, uh, um,
the current for the third phase.
And so we use,
uh, one hundred fifty amps
hall effects. And, uh,
it's it's a nice model from Allegro
Microsystems eight seven five nine.
If you ever need, uh, current,
just use them.
And this,
uh, graph shows how fast
the precision tracking is actually
working. And, um,
it basically shows on
the Y axis the position in the sensor
units. Uh, and on the
X axis, it's time.
And you see tracking is quite
nice. Blue line is the
opposition. Green Line is the desired
position.
And no major overshoots,
uh, despite the the Mean
Square WAF.
So speaking of rotation centers,
um, they also
Microsystems builds nice,
uh, effect sensors.
They come in two flavors, absolute.
And, uh.
Relative, so a but
absolute bit absolute revolution is quite
nice, but not enough.
And so they they
build rings with 128 poles
and you interpolate between two
poles and loose
the the.
Yeah, it's not absolute anymore
but pretty, pretty,
uh, accurate.
And if you combine both so on
one axis, one absolute center to
basically index uh um
the relative center you
gain. Uh yeah.
Seven to bit seventeen bit uh
absolute center which is
at least in theory, I mean this
is calculated, not measured,
really, really accurate.
And each center is about
ten dollars and the magnets
are five dollars each.
Uh, another nice thing about
those centers is that a half direct
quadrature output and all the modern
micro controllers have direct
hardware, creditor inputs or decoders.
And, uh, so you can just read the
resistor and see the
value of your SANZAR without any
decoding protocols, whatever.
Uh, a problem, though, is that
we don't know how non-linearity,
uh, will work
or is a problem in
high current switching
because the magnet of the magnet fields
antisemite.
They will produce.
And, um, that shouldn't
matter too much because the and a factor.
So the lack position won't depend
on the um
on the on the error
from the rotation sensor, but on the
mechanical areas like manufacturing
errors, uh, bending and flexing
because it's, it's not a rigid body,
it's a real thing.
And uh, another thing is that,
uh, the sampling time
of the last measurement isn't clear if
you do a digital readout, which you can
do, but,
uh, with credit to output,
that is OK because it's a fixed delay
so that the credit output has
a fixed delay from the last temporal
time.
But the simpler
the output will, uh, stop
working at higher velocities.
So velocity
calculation with those sensors
is a bit of a problem.
If anyone knows the solution for that,
tell us after the talk.
Now back to Geass
Meters.
This is really a lot
because if you just use your
usual screwdriver,
they claim they can do like 50
meters.
You will probably never reach that.
You need like 30 meters to
to screw in a screw.
And to really.
Hartwood, our motor
talk is around
two meters because we
only calculate a calculated
the meters uh, uh,
the motor talk right now.
And, um, we
need a reduction of one to fifty.
Maybe that leaves
us with not
too many gering options.
One option is
a fancy thing called, uh, harmonic drive,
also called strain wave gering.
Um, it's quite a nice thing.
It's Don Extensor expensive.
So one is probably
starting at a thousand K, but,
uh, they are
really expensive and planetary.
These girls are cheaply available,
uh, for example, in your
cordless screwdriver, but
uh have different uh
yeah.
They, they have a backlash
usually. So if you
um if you turn
the motor in a different direction
that will be play in, in the gears
and that can break
things because if if the
gear spins freely with that
talks. Yeah.
Usually just tiff's
icebreaking from the here.
That isn't a problem at all of the I want
to drive and drive and
they, they are flat whites and.
Yeah.
But uh we try
to get away with planetaria years
and then there is a one
more actuator system.
It's a linear actuator,
it's basically like hydraulics.
But without hydraulics you lose,
you use uh, screw
most common things that ball screws.
And then there's some fancy thing called
planetary roller screw, which has
a planetary gear built into
the to the screw.
We won't go, uh, go into
detail, see it, because it's a task force
on, uh, if you're
really interested in it.
Um.
So to to test all this,
we've built a motor test bet
and the motor test bed is basically a
giant pendulum.
It's a one meter long pole
where we bolt it on, uh,
10 kilograms of, like,
handlebar whites.
And so
if you do the math again, the static
torque is up to 100 meters
to test everything we want.
And the dynamic torque can obviously
be much higher because,
yeah, it has
two parts.
One for actual
axle, uh, motors like
the one that drives into the planetary
gears and one for Linera actuators
like balls, screw systems.
And, uh, yeah, as soon as
we have the first tests, we will
probably post a video material
of Breaking Geass Motors
and controllers and everything
will burn.
So, uh.
Obviously, we had a look
what exists
in the interview in the academic field
right now, and two simple
projects are Tulip and Lola
Tulip is from a university
in the Netherlands, the University of
Eindhoven. Dahlstrom, 20, and its
rather small, uh, one on a
20 centimeters 15 kg and
uh, uses, uh, interesting concept
named a serious elastic activation
in the knee, which basically meant
they couple a motor with
a spring add to, um,
conserve energy.
Uh, that is uh yeah.
During the food contact to load basically
the spring during the foot contact.
Uh but uh they lose
uh, bandwidth with this.
So they uh um
that the control is only
five to 10 hertz fast.
Uh, they get away with rush motors
because they are light enough
and, uh, planetary gears.
And they you have a
predecessor named Flame for
which I will show video shortly.
So back to the kinematics, uh,
later, uh, earlier showed, um,
the kinematics for our robot project.
Uh, it's, uh, comparable
to this basically the same three degrees
of freedom in the hip.
So this this
and you cannot show that too
good. But you can turn your hip at
one in the knee and
two and ankles.
So, uh, pitch on and
roll and, uh,
the ankle is, uh, the ankle roll
is passive, so they just
attach springs to it and no motor.
So now no it will play the
video
just a second.
This new video.
So this is flame,
as you can see, flame
moves and a slight curve.
That's because as the predecessor
of Tulip, it's oh
yeah, whatever, it misses
one degree of freedom in the hip.
So it has only two degrees
in our freedom and the hip, and that
means that it can
control it.
Movement around the Z axis.
So the rotation of the Z axis.
So it as a result
it moves in a curve because it can
control it.
Next up is.
Lola Lola is
a fully sized humanoid
one at one hundred and eighty
centimeters, 55 kilograms,
but it's to Munich.
It has really many
degrees of freedom.
Twenty five in total.
But the arms and
the that actuated as well.
And
it has seven degrees of freedom.
Polec the
nice actuation concept is borrowed
here, as you might
see on the picture.
Number five is
the ball screw and
they use it to bend the
knees and the ankles.
Think of it, uh,
at least for the knee, like on your
standard excavator.
Yeah, like you have arm
and to the arm by
reducing the piston
length or hear the
distance of the balls screw
and they use harmonic drives all
over. So everything that is not done
by a linear actuators
uses harmonic drives and
industrial grade brushless
motors.
Um, the activation
concept with all degrees of freedom is
here. Uh, basically
it's comparable.
So at least for the next, it's comparable
to Tulip and our approach,
uh, with the exception that
they have a total active
toe ajoint joint, uh,
they claim that that
this is useful to, uh, walk
faster, not an actual
target at the moment, and,
uh, climb stairs and steps,
which would be nice, but should be doable
as well without it.
So and, uh, as a special feature, the hip
that is tilted
against the X Y plain, you can see that
and the Chromatics.
We have a video for this as well.
So this is Lola, and
as you could see, as you can see, a
pushing to flame, um,
Lola never has straight
legs but
always bent knees.
And that is because a
bushman, the guy who
wrote the control of Lola,
uh, is
who we based our work on,
our controller work on.
So they it looks yeah,
they have arms.
And to conserve the angular momentum,
like we told you earlier,
looks way better, but, uh.
Yeah, yeah.
So
they already started this point.
So, um.
Now, we do that later on, so the
current status is, um,
yeah, some lation is on a good
way.
We studied existing designs.
We have built a workshop in the last year
with a milling machine, a small
lathe, a fully fledged
electronics workshop.
And the biggest challenge right now
is to to get
an activation concept that you can
pay for.
Uh, yeah.
So next steps next year
will be to burn, like I said before, to
burn motors, to burn drivers
and to test years.
And after the gear question
is solved, we will start
constructing our first pair of legs.
So.
Additionally,
in the last demo, uh, Novarro,
to you, the terrain was known
to the robot and therefore we
began building a camera system.
Um, this is the camera
with a command, uh, to
whoever knows what a seamount is.
And, um,
it's a scientific, uh, camera based on
that part of the project, uh,
build a fully open source film camera
and Fourcade resolution,
which is quite amazing.
And it's based on a simple
SANZAR with 2K resolution.
So full HD, um,
up to three hundred forty fps
12th with dynamic, uh,
color depth.
And it said it has a global shutter.
So uh, not like on
your favorite GoPro video where you see
the prop of the plane
flexing all over the screen.
It can take it takes
the image of all pixels at the same
time.
And uh, additionally, all of the line
files, uh, are available
on get up.
Just if, you know, Kickett
can just open it.
We have it with us and we'll, uh,
hack on the firmware for the rest of the
event.
And that's it.
Yeah.
OK, thank you very much.
As always, we have microphones
in the room if you have questions, so
please line up in front of the
microphones.
We also should have a signal angel in
the room who can answer
your questions if you have them on the
Internet. So Ulsterman I
on Twitter and they will be relayed here
into the room.
So our partner.
Any questions
now?
Any questions.
Well, can be a microphone
please. Line up behind microphones.
Right next.
OK, so yeah. Please.
Question about the girls actually now
about the I'm not sure of the language to
assist Malkia if they can contribute.
Um warm I think.
Yes. That's the big
problem about them is they have very
large reduction ratios that
they're inefficient because there is
friction that did the snail
is actually sliding against
the, uh, the the gear, the
wall there.
And as you saw, I mean, you have
a Wikipedia claims that there are no
50 percent effective.
So if you have 250 watts,
then this thing is going to get really
warm.
So this is the reason why they would
otherwise be ideal are, for example,
using windshield wipers for cars.
Um, but I think our power requirements
are too large and additionally,
they are self-locking.
So if
anything collides with your leg and
your leg isn't in the stance you
thought it was, it would break because
the gear won't it
won't move.
Thanks.
OK, from spin,
yeah, I just was wondering,
all of the robots were essentially
humanoid robots.
I was just wondering if other bipedal
creatures were considered like maybe an
ostrich, which evolutionarily
speaking, is actually better for bipedal
walking than the human being is
curiosity. Well, can you give me tips
for avoiding the robot apocalypse?
Well, as far as
the famous case idea puts it, I don't
think this is a problem at the moment.
So just climb up a tree
and put down some water.
But
regarding the ostrich.
I think
I think from the point
of view of the rather simplified physics,
it's much the same because,
you know, it also has two legs and the
legs similarly can
move its feet.
So I guess the physics
is very abstract levels
for me, quite similar to writing a
control, I would also probably be
similar.
So I'm not sure
if it's from a purely mechanical
point of view, much different from
a human.
Other than that, the human has arms and
it has wings.
And as you saw, I mean,
we just think of the torso as one big
rigid body and don't model the details.
Does this answer the question?
OK.
Yeah, there's a question from the IOC.
OK, are there any plans for strict
Lex robots or are they going
to stay with Bendix?
Well, this depends on the controller.
So the problem is if you have trade
legs, then you lose one degree
of freedom.
Because essentially, you can't you
can't independently control the
default position anymore, that you
just have one degree of freedom left,
which is clear. Of course.
I mean, if your leg is straight, then
the distance between the hip and the
ankle has to remain constant.
And this kind of complicates control
design.
Um, so at some point
we might get around this,
but right now it's
probably simpler to stay with bent legs
and not know this is cast in stone,
right on this control.
It's not really optimized
for building an actual robot
yet at the moment you're experimenting.
So this might be considered
in the future, but at the moment it's the
so-called singularity.
Yeah, puts us off it, even
though it looks non-human.
Again, a question here from a front line,
first of all, thank you for the talk,
then I have a question.
You said that you implemented the
algorithms from Lytro Featherstone's
originally dynamics algorithms.
And I think there is a library that
exists, the ideal rigid body dynamics
library. Do you noted or did you
have a look at it?
I know of it.
I had a quick look at it,
but it seemed to me
that it was quite slow.
And the reason
so it's not the reason why we implemented
this ourselves is because.
We make something we only consider
kinematic chanes at the moment, not
trees, and I think
this makes a low level algorithm faster
because you can just put your bodies
in a linear list.
And the RBD al, I think,
really allocates a tree structure because
it can deal with somewhat generic case of
kinematic trees.
I did not actually PROFILET.
I built it and ran it on the
example and it turned out to be
relatively slow.
I don't want to insult anyone.
Maybe that's my fault.
But I suppose
this is because in the lowest loops,
it actually does a lot of
unordered memory access, whereas if
you do kinematic change,
you just can go to the body
one after the other.
So our system is far less generic.
It's really just for this robot and not
much else.
No, no, thanks.
Okay. Do we have any other question from
the Internet?
Yes. Them to ask,
is there or will there be an integration?
Eventually, yes.
I guess
one one thing
about this, though, is all this motion
planning algorithms that we all heard
about, the last talk,
they usually focused on static
trajectories. So, for example,
like my arm, I
am an industrial robot.
My arm is here and I want to get it here.
This is this is a this is a typical
collision avoidance problem because I
can't just move straight.
I have to move like this.
And I think this is the kind of problem
that our eyes are.
But our eyes actually.
But this this satellite
images that were mentioned focuses on
the thing about this kind of trajectory
is that it doesn't matter how fast you
execute it, you can just go slow
or fast as long as you stay on the
trajectory.
And when
I looked at it, it seemed
that it didn't have algorithms for our
kind of problem that actually matters
how fast you execute that trajectory,
because just because some
walks like a walk works,
it doesn't mean that it will continue to
work. If you go half as fast
because the reaction forces change the
change, you actually have to consider
the dynamics and
all of the assembling based motion
playing I witnessed, at least, at least
as far as I know, don't
typically deal with this.
That being said, of course, I mean, it's
still a very nice option to use the rice,
for example, for this camera project,
because you can
feed it with raw image data.
And it's at
least that is what the last claimed.
Yeah. And, uh, yeah,
the whole image pipeline is within
Ross and.
Yeah, yeah.
That sounds really convenient.
And they're it really makes sense to
avoid this kind of duplication.
OK, are there any other questions?
It doesn't look like it.
So I ask her again for one round of
applause for Felix, an awkward.