TED2007
Hod Lipson: Building "self-aware" robots
ホッド・リプソン「自己概念を持ったロボット」
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ホッド・リプソンが、学習し、自己理解が可能で更に再生能力も有する、素晴らしいロボットを披露します。
Hod Lipson - Roboticist
Hod Lipson works at the intersection of engineering and biology, studying robots and the way they "behave" and evolve. His work has exciting implications for design and manufacturing -- and serves as a window to understand our own behavior and evolution. Full bio
Hod Lipson works at the intersection of engineering and biology, studying robots and the way they "behave" and evolve. His work has exciting implications for design and manufacturing -- and serves as a window to understand our own behavior and evolution. Full bio
Double-click the English transcript below to play the video.
00:25
So, where are the robots?
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さて ロボットはどこでしょう?
00:27
We've been told for 40 years already that they're coming soon.
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ロボットがまもなく現れ仕事を代行してくれると
00:30
Very soon they'll be doing everything for us.
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もう40年間ほど言われてきました
00:33
They'll be cooking, cleaning, buying things, shopping, building. But they aren't here.
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料理 掃除 買物 建築 でもどこにも見当たりませんね
00:38
Meanwhile, we have illegal immigrants doing all the work,
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代わりに違法移民が仕事をしていますが
00:42
but we don't have any robots.
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ロボットはいません
00:44
So what can we do about that? What can we say?
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さて どうしましょうか
00:48
So I want to give a little bit of a different perspective
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そこで皆さんに今までとちょっと違った
00:52
of how we can perhaps look at these things in a little bit of a different way.
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ロボットについての観点をお教えしたいと思います
00:58
And this is an x-ray picture
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さて これはX線写真です
01:00
of a real beetle, and a Swiss watch, back from '88. You look at that --
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本物のカブトムシと1988年スイス製の時計のものです
01:05
what was true then is certainly true today.
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これらは当時からなんら変わっていません
01:07
We can still make the pieces. We can make the right pieces.
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私たちは今なお正しい部品を作れますし
01:10
We can make the circuitry of the right computational power,
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コンピュータ動力の電気回路も作れます
01:13
but we can't actually put them together to make something
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しかし実はこれらを適応力があり実際に
01:16
that will actually work and be as adaptive as these systems.
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動作する集合体としては組み立てられないのです
01:21
So let's try to look at it from a different perspective.
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そこで異なる観点から見てみましょう
01:23
Let's summon the best designer, the mother of all designers.
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究極のデザイナーを召喚しましょう
01:27
Let's see what evolution can do for us.
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進化が私たちにもたらしたものを見てみましょう
01:30
So we threw in -- we created a primordial soup
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我々は棒 モーター ニューロンといった
01:34
with lots of pieces of robots -- with bars, with motors, with neurons.
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ロボットの材料をたくさん原生液に放り込みました
01:38
Put them all together, and put all this under kind of natural selection,
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これらを混ぜ合わせ 自然淘汰と変異を経たのち
01:42
under mutation, and rewarded things for how well they can move forward.
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どれだけ前進できるかによって報酬反応を与えたのです
01:46
A very simple task, and it's interesting to see what kind of things came out of that.
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単純な課題ですが実験の成果を見るのはおもしろいですよ
01:52
So if you look, you can see a lot of different machines
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この実験から生まれた異なる種類の機械を
01:55
come out of this. They all move around.
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ご覧ください みな異なる方法で
01:57
They all crawl in different ways, and you can see on the right,
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このように動き 這っています
02:01
that we actually made a couple of these things,
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これらを実際に作ってみました
02:03
and they work in reality. These are not very fantastic robots,
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素晴らしい見栄えではありませんが
02:06
but they evolved to do exactly what we reward them for:
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前進に対する報酬反応を与えた分だけ
02:10
for moving forward. So that was all done in simulation,
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シミュレーション上で 進化しました
02:13
but we can also do that on a real machine.
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実際の機械を動かすことも出来ます
02:15
Here's a physical robot that we actually
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ここに実際のロボットがありますね
02:20
have a population of brains,
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機械上で競い合い 進化する
02:23
competing, or evolving on the machine.
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頭脳部分を持っています
02:25
It's like a rodeo show. They all get a ride on the machine,
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ロデオショーみたいでしょう
02:28
and they get rewarded for how fast or how far
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機械がどれだけ速く 遠くへ前進できるかにより
02:31
they can make the machine move forward.
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報酬が与えられます
02:33
And you can see these robots are not ready
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ご覧のとおり このロボット達は
02:35
to take over the world yet, but
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仕事を引き継ぐにはまだ未熟ですが
02:38
they gradually learn how to move forward,
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次第に前進の仕方を学び
02:40
and they do this autonomously.
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自律的に動くようになっています
02:43
So in these two examples, we had basically
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さてこの2つの例を使って実際に
02:47
machines that learned how to walk in simulation,
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シミュレーションで歩き方を学んだ機械と
02:50
and also machines that learned how to walk in reality.
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現実に歩き方を学んだ機械を見ましたが
02:52
But I want to show you a different approach,
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また別のアプローチをご覧頂きたいと思います
02:54
and this is this robot over here, which has four legs.
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このロボットは 4本の脚を持ち
03:00
It has eight motors, four on the knees and four on the hip.
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膝・腰それぞれに4つのモーターを搭載しています
03:02
It has also two tilt sensors that tell the machine
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更に 2つのティルトセンサーを搭載し
03:05
which way it's tilting.
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傾きを調べます
03:08
But this machine doesn't know what it looks like.
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皆さんには4つの脚が見えますが 機械自身は
03:10
You look at it and you see it has four legs,
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自分がどんな形なのか解ってません
03:12
the machine doesn't know if it's a snake, if it's a tree,
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自分が蛇なのか木なのか
03:14
it doesn't have any idea what it looks like,
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外見に対する知識は一切ありませんが
03:17
but it's going to try to find that out.
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自らその特定を試みます
03:19
Initially, it does some random motion,
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まず 適当に動いてみて
03:21
and then it tries to figure out what it might look like.
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自らの形を探ろうとします
03:24
And you're seeing a lot of things passing through its minds,
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いろいろなことを考えているんでしょうね
03:26
a lot of self-models that try to explain the relationship
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動作と知覚の関係を説明しようとする
03:30
between actuation and sensing. It then tries to do
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自己モデリングの試みです 次いで機械は
03:33
a second action that creates the most disagreement
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第2の動きで予測とこれらの分析の
03:37
among predictions of these alternative models,
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最大の不調和を引き出します
03:39
like a scientist in a lab. Then it does that
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実験室の科学者のようですね
03:41
and tries to explain that, and prune out its self-models.
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次にこの解釈に基づき 自己モデリングを絞り込みます
03:45
This is the last cycle, and you can see it's pretty much
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この最後のサイクルで外見はかなり捉えられます
03:48
figured out what its self looks like. And once it has a self-model,
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いったん自己モデリングを確立すると それを
03:52
it can use that to derive a pattern of locomotion.
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運動パターン抽出のために利用します
03:56
So what you're seeing here are a couple of machines --
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つまりご覧頂いているのは機械であり
03:58
a pattern of locomotion.
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運動パターンなのです
04:00
We were hoping that it wass going to have a kind of evil, spidery walk,
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蜘蛛の様に繊細で邪悪な歩き方を期待していましたが
04:04
but instead it created this pretty lame way of moving forward.
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代わりにこのまどろっこしい前進法を生みました
04:08
But when you look at that, you have to remember
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ところで思い出してください
04:11
that this machine did not do any physical trials on how to move forward,
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この機械は前進の仕方を試行したことがありません
04:17
nor did it have a model of itself.
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自己認識もありませんでした
04:19
It kind of figured out what it looks like, and how to move forward,
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自らの形と前進法を何とか割り出して
04:22
and then actually tried that out.
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実際にそれを実行したのです
04:26
(Applause)
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(拍手)
04:31
So, we'll move forward to a different idea.
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それでは また別のアイデアに移りましょう
04:35
So that was what happened when we had a couple of --
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ご覧頂いたのは私たちがいくつか…
04:40
that's what happened when you had a couple of -- OK, OK, OK --
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何が起こったかというと… OK わかった
04:44
(Laughter)
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(笑)
04:46
-- they don't like each other. So
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仲が良くないみたいですね
04:48
there's a different robot.
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さてここに別のロボットがあります
04:51
That's what happened when the robots actually
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先程ロボットの行動に対して報酬反応が
04:53
are rewarded for doing something.
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与えられたらどうなるかを見ました
04:55
What happens if you don't reward them for anything, you just throw them in?
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報酬反応を与えず 原生液に放り込めばどうでしょう
04:58
So we have these cubes, like the diagram showed here.
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ここに図で示されているような立方体があります
05:01
The cube can swivel, or flip on its side,
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この立体は面上で回転することができます
05:04
and we just throw 1,000 of these cubes into a soup --
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ではこの立方体を1000個 原生液に入れてみましょう
05:08
this is in simulation --and don't reward them for anything,
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報酬反応を無くした シミュレーションです
05:10
we just let them flip. We pump energy into this
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動き回らせておき エネルギーを注入します
05:13
and see what happens in a couple of mutations.
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変異を繰り返した後どうなるでしょう
05:16
So, initially nothing happens, they're just flipping around there.
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最初は何も起きません ただ動き回っているだけです
05:19
But after a very short while, you can see these blue things
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しかし少し経つと 右側の方で青色のものが
05:23
on the right there begin to take over.
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増えているのが見えますね
05:25
They begin to self-replicate. So in absence of any reward,
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増殖を始めたのです 外部からの報酬がなければ
05:29
the intrinsic reward is self-replication.
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内因性刺激として 自己増殖を行います
05:32
And we've actually built a couple of these,
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実際このようなものを造ってみました
05:33
and this is part of a larger robot made out of these cubes.
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立方体から作られた大きなロボットの一部です
05:37
It's an accelerated view, where you can see the robot actually
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早送りですが 実際にロボットが自己増殖を
05:40
carrying out some of its replication process.
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行っているのがご覧いただけます
05:42
So you're feeding it with more material -- cubes in this case --
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この場合は立方体ですが もっと部品とエネルギーが
05:46
and more energy, and it can make another robot.
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あればまた別のロボットを作れます
05:49
So of course, this is a very crude machine,
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もちろん これは大まかなものですが 我々は
05:52
but we're working on a micro-scale version of these,
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ミクロの世界でも研究を行っています
05:54
and hopefully the cubes will be like a powder that you pour in.
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そしてこの立方体を粒子程にしたいと考えています
05:57
OK, so what can we learn? These robots are of course
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さてここから何が学べるでしょう これらのロボットは
06:02
not very useful in themselves, but they might teach us something
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このままではあまり使い物になりませんが より高性能な
06:05
about how we can build better robots,
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ロボットの作り方 もしくは人間や動物の
06:08
and perhaps how humans, animals, create self-models and learn.
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自己モデリングの仕方に繋がるかもしれません
06:13
And one of the things that I think is important
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そして大切なことの1つは
06:15
is that we have to get away from this idea
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機械を人間の手で作り出すという
06:17
of designing the machines manually,
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考えから離れ ロボットを子供のように
06:19
but actually let them evolve and learn, like children,
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自由に進化 学習させるということ それが
06:22
and perhaps that's the way we'll get there. Thank you.
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ロボットのある未来に繋がります ありがとうございました
06:24
(Applause)
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(拍手)
ABOUT THE SPEAKER
Hod Lipson - RoboticistHod Lipson works at the intersection of engineering and biology, studying robots and the way they "behave" and evolve. His work has exciting implications for design and manufacturing -- and serves as a window to understand our own behavior and evolution.
Why you should listen
To say that Hod Lipson and his team at Cornell build robots is not completely accurate: They may simply set out a pile of virtual robot parts, devise some rules for assembly, and see what the parts build themselves into. They've created robots that decide for themselves how they want to walk; robots that develop a sense of what they look like; even robots that can, through trial and error, construct other robots just like themselves.
Working across disciplines -- physics, computer science, math, biology and several flavors of engineer -- the team studies techniques for self-assembly and evolution that have great implications for fields such as micro-manufacturing -- allowing tiny pieces to assemble themselves at scales heretofore impossible -- and extreme custom manufacturing (in other words, 3-D printers for the home).
His lab's Outreach page is a funhouse of tools and instructions, including the amazing Golem@Home -- a self-assembling virtual robot who lives in your screensaver.
Hod Lipson | Speaker | TED.com