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Turns out that doing In-context learning for robots is not that hard...

273,623 次观看 • 17 天前 •via X (Twitter)

25 条评论

ismaelvega 的头像
ismaelvega17 天前

it's right behind me isn't it

Chenhao Li 的头像
Chenhao Li17 天前

@chris_j_paxton I feel this is a not very well-defined area where we know how “OOD” the task is.

Chongkai Gao 的头像
Chongkai Gao17 天前

What's the difference between in-context learning and those one-shot imitation learning papers from several years ago? I think there demo are very similar:

Kamal Gupta 的头像
Kamal Gupta17 天前

I like the robot wearing meta glasses. Also wuji hands?

Isaac Sin 的头像
Isaac Sin17 天前

nice @BillyYYan

Daneel 的头像
Daneel17 天前

Oh my! Third in context learner robot in the last few weeks and first I’ve seen with a humanoid. Nice!

Samay Maini 的头像
Samay Maini17 天前

@BillyYYan this is great!

seanpixel 🫧 的头像
seanpixel 🫧17 天前

@chris_j_paxton are those Aria glasses?

Anto Patrex 的头像
Anto Patrex17 天前

Very true. But the real question is: how do we scale this data, and how accurate and reliable are these actions once they’re deployed in real production environments?

Ishmael59 的头像
Ishmael5917 天前

What happens if you move the bowl?

Aryan Dhawan 的头像
Aryan Dhawan8 天前

How sensitive is it to the context you give it? Picking an example that actually disambiguates the task seems like it could become half the work.

Joseph Sottile 🌊 | Diffraction 的头像
Joseph Sottile 🌊 | Diffraction17 天前

@chris_j_paxton Any reason for having your hands in that particular starting/neutral position?

Puzzle Paws 的头像
Puzzle Paws17 天前

man, 'not that hard' on a clean table with two objects. real world generalization is the hard part. my cats would break this in ten seconds flat.

Armeen 的头像
Armeen13 天前

In-context adaptation is the most promising route past per-task finetuning. The eval that would convince me: a demo given in one room, then executed in a room the policy never saw, with the object swapped for a same-category variant.

/ 的头像
/17 天前

do a more complex grasp, like a hammer or something. Squishing a soft toy is trivial.

Roman Li | AI-powered GTM 的头像
Roman Li | AI-powered GTM16 天前

Impressive. The product question is no longer whether a robot can learn in context, but whether a non-expert user can provide the right demonstration reliably. The teams that turn that teaching loop into a simple, observable UX will have a real adoption advantage.

Samuel Ekpe 的头像
Samuel Ekpe17 天前

@xiaolonw This is cool

Oliver 的头像
Oliver16 天前

And how does it look without the context?

nnnnn 的头像
nnnnn16 天前

can it learn to wash the dishes this way?

Robert Blank 的头像
Robert Blank17 天前

Just add some legs to increase the cost and complexity and you’ve got a humanoid

Andrew I. Christianson 的头像
Andrew I. Christianson17 天前

VLA model?

Sadly life respecter 的头像
Sadly life respecter17 天前

What robot is this. The arms look very human like

Krishna Nagam 的头像
Krishna Nagam16 天前

Now I understand where robots get such robotic movements. Infact robot movements are more human like than the person teaching it.

Weijie Wang 的头像
Weijie Wang15 天前

what is the Glasses, it is the secrete , how this demo work

ovoDRIZZYxo 的头像
ovoDRIZZYxo17 天前

u need another bot, thats taking it out

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