7-3 Actuated Puppet

Loading...

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

Our next topic is Actuated Puppet.

The what we introduce here is an Actuated Physical Puppet as an Input Device for

Controlling a Digital Manikin.

The Motivation here is that 3D character posing is difficult.

So, suppose you have a character that you want to set the posing.

And the most popular approach I think is 3D widgets.

On the screen, you have a widget like this, and

you use a mouse to control individual joy dongles.

However, this is very tedious to control individual joint dongles.

For each joint, you have three angles, one by one, can take much time.

Another approach is the Motion capture.

You have a capture device and a unit.

However, you need dedicated environment, and

that you also need a skilled actor, actor.

And also another possibility is to use Physical Input Device.

This is very convenient.

You can grab a Puppet and then pose, and you get the 3D view.

So you can use both hands can be very good.

However standard physical input devices very stiff.

You know if you, if it to relax, it cannot hold a pose.

And if its stiff enough to hold a pose, it's very hard to control.

And also you have to control, manually, space for

all joint angles, which is inconvenient, in some cases.

So, our approach is to add several motors to each joint, so

we get an Actuated Physical Puppet.

So we not only use Puppet as input device, but

also the device reacts to the physical batch of data to provide active feedback.

And there are couple benefits to it.

First, it's easy to load existing pose into the robot, so

you can start from existing pose to accelerate posing.

So it's one benefit.

Another is intelligent gravitational compensation.

Here's a little bit explanation.

As I said, if the joint is all free, it's easy to control.

But you know, the it cannot hold it's posture.

So it's inconvenient to And

then another approach is always approach turn off the sabo motors.

Then it can be too stiff.

It can hold existing pose, like this legs.

However as a negative side it very hard to control, change a pose.

So what we do is, adaptively do it, intelligent gravitational compensation.

So if you do not touch this arm, these legs, its con,

fixed by a sabo motor by producing a power against grav, gravity.

However, if the user start to manipulating the joint,

the system automatically detects it.

And then automatically turns off some sabo motors, so

you can push a berry with very light weight force.

So, that is intelligent gravitational compensation.

And then finally, we also implemented active guidance,

using measured human data.

And this consist of joint coupling, and data driven inverse kinematics.

Let me describe a little bit more.

So joint coupling is two joints work together.

What's easy to understand the example is this hip joint, and knee joint.

You know, as you know if you move your leg.

Upwards you if you control hip joint, then knee joint also follows, right?

It's very hard to keep the legs straight as you rotate the hip joint.

So, if you rotate the hip joint,

it's very natural to also bend the knee joint together.

So, this is joint coupling.

And with actuation you can do it, you know,

if you control the hip joint, the system will automatically rotate the knee joint.

This is joint coupling,

which is very useful to generate very natural human posture.

So this one automatically avoids, unnatural configuration.

The another is, data driven inverse kinematics.

If you just control position.

For example, suppose you control the fingertip, and they go downwards, and

if you apply simple inverse kinematics result is like this.

So it's mechanical value.

Geometric, geometrically bodied.

Finger tip follows the user control.

However, joint only rotates along the alo, along the arm.

And then, resulting pose is very strange, a unnatural force.

And instead of that, we use data.

We measure up, lots of natural reaching gesture, over humour.

And the user synthesize appropriate motion.

So here if the user pull down the fingertip downwards, and

then the character, the robot, automatically puppet

moves making a very natural motion, using data.

So, let me show you a video.

So as I said Physical Puppet not only controls a physical body of character,

also body of character provides feedback to the Physical Puppet to

guide not to oppose anything.

[INAUDIBLE] To the eight degrees of freedom and connect to the PC.

Yeah so primarily it's an input device.

You control the pulse, and then you sink [INAUDIBLE] up the same pulse.

So this provides a very intuitive way, of controlling character posture.

This can be very tedious, if you use two dimensional mouse.

And one benefit of using sabo motors is,

uploading pre defined postures into a robot, our puppet.

So, you can get pre-defined postures in to the puppet.

So this is a very good starting point for

design, and then this is Gravitational Compensation.

You know, if the Torque is OFF to relax then it cannot hold a given posture,

you know, if you release it goes down.

And on the other hand, if you to, always turn on the torque power,

then it can hold the posture, but, very rigid, you know?

You need strong power.

To rotate.

And this is a result of automatic gravity compensation.

So as you start pushing.

The system turns off the power.

So, you can change the pose, with very light weight force.

But also, system can hold the posture after release.

And then, this is a result of Data-driven IK.

So, if the Data-driven IK is turned off.

The motion is very unnatural.

It's very robotic motion, because the robot doesn't change entire body shape.

However. If you turn on the inverse driven,

Data-driven IK, then the resulting motion is vary natural.

So user control the fingertip, and then system automatically bend his legs and

then move the head and other arm.

And this is a joint coupling.

The user is controlling the hip joint, and

the system automatically controls knee joint, so.

So, let me show you a couple application scenarios.

So, suppose you design in car seat, and then the visibility should be preserved.

So this, in this way,

you can intuitively control the detail monitoring in the car environment.

And you can check the here, is reachability.

So you can check whether this puppet can meet specific target in the car.

So this one is load assessment.

So this led indicates where the power is necessary to hold the posture.

So, this is useful for analysis of environment.

And this is a Visibility analysis.

So by changing the posture of puppet,

we can see which part of the environment the puppet can see.

So that's the idea.

So let be briefly describe the algorithm.

Specifically, I will briefly describe how to do Data-driven Inverse-Kinematics.

So as I said, Data-driven I case like this.

If you move a fingertip, then the puppet makes a very natural human like motion.

In order to do so.

We get data.

But let me first describe Inverse-Kinematics in general.

So in that Inverse-Kinematics, is a very fundamental program,

in character animation or also in robotics.

So suppose you have this kind of arc, you have a base here,

and then you have joint one, joint two and joint three.

And you have each joint angles.

So, Forward Kinematics is the standard way.

So, given joint angles, you get the position of the fingertip.

So, this is fairly deterministic single computation.

However, what people usually want to do is to specify target x,

y position of fingertip.

And then try to convert appropriate joint angles.

This is very infrequently occurring problem to solve.

And then it looks like this, given x, y, you have gone to joint angles.

This is inverse problem, which is very difficult to solve.

For many solutions,

many other And there are couple possible ways like purely geometric.

So something like, minimize a change from the current configuration or

physically most you know, physically most plausible shape, or as a data-driven.

And Data-driven is a method we use.

So, we do some synaptics.

In the motion capture environment, we ask for the person to reach many places

around his body, and then we measure all the postures, to get always position.

So you get many data like this, x, y, z position, and joint angles, x, y,

z position, and joint angles.

We get many data of these samples.

And after that user input is target fingertip location.

And then we are identify the nearby samples.

And then just blend them together.

And then you'll get, a desired joint angle.

So this is what we do in the Data-driven Inverse-Kinematics.

So here's the summary.

So we presented, I presented actuated puppet device for character posing.

The user controls a virtual character, using physical puppet, and

also gets feedback from the system.

And one important feature is intelligent gravity compensation, and

active guidance from the system.

And we, I present the Data-driven Inverse Kinematics.

So, we take many snapshots, example for

this, and then blend them together to get natural posture.

So, to learn more, original paper is titles,

An Actuated Physical Puppet as an Input Device for Controlling a Digital Manikin.

And if you want to know more about the previous standard puppet devices,

there's two papers.

Dinosaur Input Device.

And those of mice and donkeys, and

specialize in the device for [INAUDIBLE] for the animations.

And Inverse Kinematics, as I said, lots of work on these topics.

So example base like is this one.

Artist Directed Inverse Kinematics using Gladiator basis function interpolation.

So this is an example of a Data-driven Inverse ICAD.

And also there is a geometric approach.

So natural Motion, Animation Solute Constraining, and Deconstraining at will.

So this one introduces a purely geometric approach,

to get natural posture from finger tip positions.

That's it for this week.

I guess [INAUDIBLE].

Explora nuestro catálogo

Inscríbete de manera gratuita y obtén recomendaciones personalizadas, actualizaciones y ofertas.

Coursera brinda acceso universal a la mejor educación del mundo, al asociarse con las mejores universidades y organizaciones, para ofrecer cursos en línea.

© 2018 Coursera Inc. Todos los derechos reservados.

Descargar en la App Store

Consíguelo en Google Play