7-4 Robotic Light

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Del curso dictado por The University of Tokyo

Interactive Computer Graphics

160 calificaciones

The University of Tokyo

Interactive Computer Graphics

160 calificaciones

Computer graphics can be a powerful tool for supporting visual problem solving, and interactivity plays a central role in harnessing the users' creativity. This course will introduce various interactive tools developed in computer graphics research field with their design rationales and algorithms. Examples include enhancements to graphical user interfaces, authoring tools for 2D drawings and 3D animations, and interactive computer-aided design systems. Rich live demonstrations and course assignments will give you insights and skills to design and implement such tools for your own problems.

De la lección

Real-world Interaction

Computer-operated appliances, such as robotic cleaners, are gradually spreading to general households in recent years. These emerging technologies have opened the door to the new research area, i.e. research on the interactions between ordinary people and robots. In this module, we will discuss interactive techniques and systems for real world interaction. Topics include: a command card interface for robot control, style-by-demonstration for robot behavior design, an actuated puppet device for character posing, a painting interface for robotic lights, and a fur display.

7-1 Command Card Interface14:27

7-2 Style-by-Demonstration8:20

7-3 Actuated Puppet12:21

7-4 Robotic Light10:35

7-5 Fur Display9:32

Conoce a los instructores

Takeo Igarashi
Professor
Department of Computer Science, Graduate School of Information Science and Technology

Next topic we discuss is control of robotic lighting system.

The work we introduce here is titled Lighty, A Painting Interface for

Room Illumination by Robotic Light Array.

So as a program, we want to address here is that it is difficult to

control many light, especially when they can change orientations.

So here an example destination, so suppose we have many lights in the ceiling,

and the individual lights can change its orientation.

And this is a very huge control parameter space, and

it's very hard to control appropriate parameters.

And typical user interfaces look like this.

You have many sliders to change brightness and orientation, and so on.

And this is not very useful for

people to quickly sketch desired lighting configuration.

So our approach is to use painting interface.

So configuration looks like this.

We have an environment, and

we have many robotic lights, and they can change brightness and orientation.

And we have a camera here to capture our environment.

And then in this kind of view, there's a paint, desired lighting this up.

So this part should be bright, this should be, this part should be dark.

We just paint desired configuration.

And then the system learns inverse relation, and

then obtain desired parameter setting.

So that's the idea.

Let me show you a video.

So again, this is a system work view.

So you have actuated the light on the ceiling and the UI in the environment.

And then you would attempt to control the light you pick up a tablet and

they use these environment and they paint on it.

So this is a prototype hardware we developed.

We the, we built a miniature room with miniature lights and

miniature furniture and this is a array of robotic lights.

So, they can change orientation individually, and

also can change brightness.

So, each light has three degrees of freedom.

So if we have 12 light, which means 36 yeah, degrees of freedom.

So, here's a painting user interface.

So, given this screen, you're going to pick up a color and paint.

Yeah.

Okay. So here lots of things happening here.

So this view is always a real-time capture of the camera view.

So you see camera view.

And as I use a paint,

user's paint is actually feedback is given as these quanta lines.

So this area bright, this area is a little bit dark.

And then given this control on user request, system continuously learns

optimization to get desired parameters, and it's a real-time.

The environment con moves around the light.

And then up, and you see the result in real-time in this camera view.

So, there's a lots of happening behind the scene.

Yeah, so user paint system searches for the, the parameter setting interactively.

And then you see the layout result immediately.

So, you always see the camera view.

Yeah. So, depending what the user input,

system automatically computes the parameters said and

then derives the robotic system.

So, this is more rapid example.

So, if you touch down, and move around, it will actually paint uncommitted.

But, if you hover, you paint it on top of the surface, you can still see

the preview of the painting without actually committing the paint.

So this is a mimic of oh, simulation of traditional approach.

So you have 12 lights and

then you individually control brightness and orientation.

So, and this a typical interface, and

it's very tedious control one by one, to get the desired result.

So we compare this interface with our painting interface, and

if you want to bright somewhere, if you want to illuminate somewhere,

it's relatively easy.

You just turn on, you edit right, and then delete it.

However, if you want to make some part dark,

it suddenly turns out to be very difficult using traditional interface because it

involves control of many lights.

You know, you move like sideways, moving away, looking away and turn off.

But there's lots of interruptions behind, between multiple lights, so

that's a difficulty.

Okay, so that's the video I think.

So all the benefit of our system is that in addition,

make specific regions bright, you can also make specific regions dark.

This is kind of negative light.

So you ask a specific region to get darker, and the system do it,

and looks like a negative light, and if you, figure it out from our user study,

in the painting if the user asked to make up a upper left corner dark,

just paint a region darker and you get this result.

However, if you use traditional direct controller, it takes time and

it's very difficult to get this kind of result.

And let me briefly describe the algorithm, behind the scene.

So, this is what's happening.

So, this is the light parameters, so you have many parameters.

Light orientations, brightness and so on.

And then if you derive the light, and if you get this out, then you know,

physics will happen and then you will get this camera view.

And then, after that I use a paint, desired painting result.

So, system compares these two, and then optimize the parameter setting and

then we'll get it.

And in order to run optimization, you need to do this iteratively many times, so

instead of using actually driving physical lights, we run simulation.

You know, what happens if this parameter setting is given,

and the system simulates the illumination result and then compare the result compare

it to the user input, and then again last simulation and so, so that's the idea.

So the physical process again is the process,

physical process is too complicate to obtain analytic model.

So in order to do this simulation, we use a data-driven prediction method.

So we, so we capture many, many illumination results.

So you have many parameters, like lighting parameters.

So this light and brightness, the second right query orientation.

So you have many parameters and then this, you get many, many camera views.

So you capture many images.

And then based on this data, you predict the resulting elimination results for

a new given parameter set.

And in order to do this, we basically individually control

the lighting parameter for individual light, and then we add them together.

However, important point is

naive summation of image data where pixel values does not work.

So suppose you have pixel A illumination rays out here, no, suppose you have

a illumination result over A here and then illumination result over light B here.

But id you are tired of, to doing pixel values, it does not

correspond to the result, eliminated by light A and B simultaneously.

That is because of the non-linear radiation set between radiance.

So, physical physical value of the brightness is not directly linear.

Relate, linearly related to the pixel value, because there's a non-linearity.

So in order to handle this,

you, we first need to convert pixel values into a radiance value.

So re-award brightness value.

After converting pixel value to radiance value you

can accurately predict the summation of two light illumination.

Then after that we can again convert it to the pixel body to get the prediction.

So that's what you need to do to get this kind of system.

So in summary, we, I just shows Robotic lighting system with painting interface.

And I briefly discussed what you need to do, to implement this kind of thing.

So, you need to compute a simulation in the radiance space,

instead of pixel space, pixel brightness value space.

So the reason a paper was published as design and

the enhancement of painting interface for room lights.

And if you want to know more about Radiance Computation, one,

one good starting point is this paper,

recovering high dynamic range, dynamic radiance maps from photographs.

So this paper discuss, hot to compute original radiance values from

multiple photographs, over the same scene.

And also our lighting control with painting interfaces, there are a couple

experiments in 3D graphics, and one example is this one, lighting with paint.

So user paints desired lighting result, and

the system computes, appropriate lighting for the computer graphics.

So what do we do is a real world version of this one.

So that's it for this week

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