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Ted Audio Collective. You're.

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Listening to tear ducts daily. I'm your host.

0:12

Elite few. It turns out

0:14

that the things human babies can

0:16

master like picking up tiny blocks

0:18

are giant challenges for robots. In

0:20

his twenty twenty Three talk from

0:22

Ted ex marine, the roboticist Can

0:24

Goldberg takes us through how advances

0:26

in a a deep learning and

0:28

lead to big strides in training

0:31

robots to do even the most

0:33

precise tasks like and tying tangled

0:35

cables. I don't know about you,

0:37

but if robots could untangle my

0:39

necklace is, I'd line up for

0:41

that for sure. Hear. Us talk

0:43

after the break. Support.

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1:58

have a feeling most people. This room.

2:00

would like to have a robot

2:03

at home. Be nice to be

2:05

able to do the chores and

2:07

take care of things. Where are

2:10

these robots? What's taking so long?

2:12

I mean we have our try

2:14

corridors and we have satellites. We

2:17

have laser beams. but where are

2:19

the robots? I

2:22

mean okay way we do have

2:24

some robots in our home by

2:26

tie. Not really doing anything that

2:28

exciting. Okay, South.

2:31

I've been doing research at U

2:33

C Berkeley for thirty years with

2:35

my students on robots and and

2:38

next ten minutes I'm gonna try

2:40

to explain the gap between sixteen

2:42

and reality. Than. The

2:44

Fields: There's something that explains this

2:46

that we call more of x

2:49

paradox and that is what's easy

2:51

for robots. Like being able to

2:53

pick up a large object large

2:55

heavy objects is hard for humans.

2:58

But what's easy for humans like

3:00

being able to pick up some

3:03

blocks and stab them were. turns

3:05

out that is very hard for

3:08

robots. And this

3:10

is a persistent problem. so the

3:12

ability to grasp arbitrary objects is

3:14

a grand challenge for my field.

3:16

Now by the way, I was

3:18

a very klutzy kid. I I

3:20

was the I would drop things

3:22

every time someone would throw me

3:24

a ball. I would drop it.

3:26

I was the last kid to

3:28

get picked on a basketball team.

3:30

I'm I'm still pretty classy actually,

3:32

but I have spent my entire

3:35

career studying how to make robots

3:37

less clumsy. Now

3:39

let's start with the hardware. So

3:41

the hand. It's a lot like

3:43

our hand and it has a

3:45

lot of motors, a lot of

3:47

tendons and cables, so it's unfortunately

3:49

not very reliable. It's also very

3:51

heavy and very expensive, so I'm

3:53

in favor of very simple hands.

3:56

So this has just two fingers

3:58

known as a parallel job. Or

4:00

so. It's very simple. it's

4:03

lightweight and reliable, and is

4:05

very inexpensive. No, actually,

4:07

it industry the Zebra super robot

4:10

gripper and that's the suction cup

4:12

and that only makes a single

4:14

point of contact. So again, simplicity

4:17

is very helpful in our field.

4:19

Scout let's talk about the software

4:21

and this is where it gets

4:23

really, really difficult because of a

4:26

fundamental issue which is uncertainty. There's

4:28

uncertainty and the control. There's uncertainty

4:30

and the perception. And

4:32

there's uncertainty in the physics. How would

4:35

I mean by the control? Well, if

4:37

you look at a robot's gripper trying

4:39

to do something, it's big, There's a

4:42

lot of uncertainty. the cables and the

4:44

mechanisms that cause very small errors and

4:46

these can accumulate and make it very

4:49

difficult to manipulate things. Snow.

4:51

In terms of the censors, yes,

4:54

robots have very high resolution cameras

4:56

just like we do and that

4:58

allows them to take images of

5:00

scenes in traffic or retirement center

5:02

or in a warehouse or in

5:04

are operating room. but these don't

5:06

give you the three dimensional structure

5:08

of what's going on. So

5:11

recently that with the new development called

5:13

My Dark and this is a new

5:15

class of cameras that use light beams

5:17

to build up of three dimensional model

5:20

of the environment. And

5:22

these are fairly effective if really

5:24

word a breakthrough in our field.

5:26

but they're not perfect so if

5:28

the objects have anything that signee

5:30

or transparent with any like acts

5:32

in unpredictable ways and ends up

5:35

with noise and holes in the

5:37

images. So these aren't really the

5:39

silver bullet and there's one other

5:41

form of censor out there now

5:43

called a Tactile Senses and these

5:45

are very interesting. They use cameras

5:47

actually image best surfaces as a

5:49

would be contact but these are

5:51

still. In their infancy. Now

5:54

the last issue is the physics. We take

5:56

a bottle on a table and we just

5:58

pushes and a robot. Putting in exactly

6:00

the same way each time, but a

6:03

bottle ends up in a very different

6:05

place each time. And why is that?

6:07

well is because it depends on the

6:09

in microscopic surface typography underneath the bottle

6:11

as it slid. For example, if you

6:14

put a grain of sand under their,

6:16

it would react very differently than if

6:18

there weren't a grain of sand and

6:20

we can't see if there's a grain

6:22

of sand because it's under the bottle.

6:25

It turns out that we can predict

6:27

the most heard of an asteroid a.

6:30

Million miles away, Far

6:32

better than we can predict the motion

6:34

of an object as it's been grass

6:36

by robots. Now

6:39

let me give you an example. Put

6:41

yourself here into the position of being

6:43

a robot. Are trying

6:46

to clear the table and your sensors

6:48

are noisy an imprecise. Your apps, waiters,

6:50

your tables and motors are are uncertain

6:52

see, can't fully control your own ripper

6:55

and there's uncertainty in the physics so

6:57

you really don't know what's gonna happen.

6:59

So it's not surprising that robots are

7:02

still very clumsy. Smothers

7:04

one sweet spot for robots and that

7:06

has to do with ecommerce. And

7:09

this has been growing as a huge trend

7:11

and during the pandemic it really jumped up.

7:13

I think most of us can relate to

7:16

that. We started ordering

7:18

things like never before had. This

7:20

trend is continuing and that Salinger

7:22

is to meet the demands, we

7:24

have to be able to get

7:26

all these packages delivered it of

7:28

timely manner. And the

7:31

challenges that every package is different.

7:33

every orders difference so you might

7:35

order some. Some nail polish

7:37

and an electric screwdriver. And those two

7:39

objects are going to be somewhere inside

7:42

one of these giant warehouses. And what

7:44

needs to be done is somewhat us

7:46

to go and find the mail past

7:49

and then God find the screwdriver, bring

7:51

up together, put him into a box

7:53

and deliver them to you. So this

7:55

is extremely difficult, it requires grasping. So

7:58

today this is almost entirely. With

8:00

humans in a human so like doing

8:02

this work. there's a huge amount of

8:05

turnover so it's a challenge and people

8:07

have tried to put robots into warehouses

8:09

to do this work has ensure that

8:11

all that well but some my students

8:14

it i. About five years ago we

8:16

came up with a method using advances

8:18

in a deep learning to have a

8:20

robot essentially train itself to be able

8:23

to grasp objects and the idea was

8:25

at the robot would do this and

8:27

simulation. It was almost as if the

8:29

robot. Would dreaming about how to grasp

8:32

things and learning how to grasp them

8:34

reliably. This is a system called Decks

8:36

Nets that is able to reliably pick

8:38

up objects that we put into these

8:40

been in front of the robot. These

8:42

are objects it's never been trained on.

8:45

And it's able to pick these

8:47

objects up and reliably clear these

8:49

pins over and over again. So

8:51

we're very excited about this results

8:53

and the students and I went

8:55

out to form a company and

8:57

we now have a company called

8:59

Mb Robotics and what we do

9:01

is make machines that use the

9:03

algorithms a software we developed at

9:05

Berkeley to pick up packages and

9:07

this is for ecommerce. The packages

9:09

arrive at large pins all different

9:11

shapes and sizes and they have

9:13

to be picked up scanned. And input

9:16

into smaller beds depending on the zipper. We

9:19

now have eighty of these machines

9:21

operating across the United States. sorting.

9:24

Over a million packages a week. Now

9:27

that's that's some progress. But it's

9:29

not exactly the com robot that

9:31

we're all been waiting for. So

9:33

I wanna give you a little

9:35

bit of idea of some of

9:37

the new research that we're doing

9:39

to try to be able to

9:41

have robots more capable in homes.

9:43

And one particular challenges being able

9:46

to manipulate the former baathists like

9:48

strings in one dimension, two dimensional

9:50

seats in three dimensions. like like

9:52

like fruits and vegetables. So we've

9:54

been working on a project is.

9:56

To untangled knots and what we do is

9:58

you take a table. We put that.

10:01

In front of the robot, it has to

10:04

use a camera to look down, analyze the

10:06

cable, figure out where to grasp it, and

10:08

how to pull it apart to be able

10:10

to untangle it. And this is a very

10:12

hard problem because the table as much longer

10:14

than the reach of the robot so has

10:16

to go through and manipulate. managed to slack

10:18

as it's working and I would say this

10:20

is doing pretty well. It's gotten up to

10:22

about eighty percent success when we give it

10:24

a tangled cable at be able to untangle.

10:28

The other one is something I think

10:30

we also or waiting for robot to

10:33

for the laundries. Now roboticist have actually

10:35

been looking at this for a long

10:37

time and there was some research that

10:39

has be done on this but the

10:42

problem is that it's very very slow

10:44

so this is about three to six

10:46

folds for our ogre s so are

10:49

we decided to free to revisit this

10:51

problem and try to to have a

10:53

robot work very fast. So what are

10:55

the things we did was try to.

10:58

Speak about a to our draw by the

11:00

could swing that fabric the way we do

11:02

with were folding and then we also use

11:04

friction in this case to drag the fabric

11:07

to have to smooth out some wrinkles And

11:09

then we bars Trust which is known as

11:11

the two seconds holes. You might have heard

11:13

of this. It's amazing because the robot is

11:15

doing exactly the same thing and that's a

11:18

little bit longer so we're making some progress.

11:20

Him. And last

11:22

example, his baggage. So you all it's counter

11:24

this all the time you go to a

11:26

corner store you have to put something in

11:29

a bag. Now it's easy again for humans.

11:31

but it's actually very very tricky for robots

11:33

because for humans you know how to take

11:35

the bag and how to manipulate it. But

11:37

robots a bag can arrive in many different

11:40

configurations and for hard to tell what's going

11:42

on. And for the

11:44

robots figure out how to open up

11:46

that bag. So what we did was

11:48

we had a robot train itself by

11:50

we painted one of these bags with

11:52

fluorescent paint and we had fluorescent lights

11:54

and with turn on and off in

11:56

the robot would essentially teach itself how

11:58

to manipulate these bags. And

12:00

so we've got it. now. up to

12:02

the point where we're able to solve

12:05

this problem about half the time so

12:07

it works, but of say it's still

12:09

we're still not. We're still not quite

12:11

there yet so I wanna come back

12:13

to more Bucks. Paradox with easy for

12:15

Robots is hard for humans and was

12:17

easy for us is still hard for

12:20

robots. We have

12:22

incredible capabilities, were very good

12:24

at manipulation. By

12:27

robots still are not there.

12:29

I want to say I

12:31

understand. It's been six

12:33

years and we're still waiting for

12:36

the robots that the Jetsons had.

12:38

Why is this difficult? We need

12:40

robots because we want them to

12:42

be able to do tasks that

12:45

we can't sue or we don't

12:47

really want to do. But

12:49

I wanted to keep in mind that these

12:52

robots they're coming. Just. Be

12:54

patient because we want the

12:56

robots. but robots Awesome! The

12:58

Us to do the many

13:01

things that robots still can't

13:03

to. Say to.

13:10

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