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Gpt-6 astra can do in-context learning on mobile manipulation! • Different environment • Different camera angle • Different layout No text prompt, it infers from video It even chooses when to use end-effector or joint space Trace recording after ⬇️

67,278 次观看 • 15 天前 •via X (Twitter)

26 条评论

Axel 的头像
Axel15 天前

The agent is asked to establish a plan based on the demonstration only It self-corrects and retries if needed Honestly it's pretty mesmerizing to see, and it will keep getting faster

Axel 的头像
Axel15 天前

The code is available here: We haven't completely cleaned it yet but your favorite agent will be able to understand our approach

Dhruv Diddi 的头像
Dhruv Diddi15 天前

Nicely done! 💯🦾👏

David Dobáš 的头像
David Dobáš15 天前

Look at the beautiful phone teleop interface

Axel 的头像
Axel15 天前

woah who built that

Robert Scoble 的头像
Robert Scoble15 天前

It is getting faster! Congrats.

Axel 的头像
Axel14 天前

🫡

Andrew Lyubovsky 的头像
Andrew Lyubovsky15 天前

Pretty Cool !!

Miguel Gregori 的头像
Miguel Gregori14 天前

Cuando el entorno cambia y el robot no pide un manual nuevo, ahí hay diseño.

NAMAN RAJ 的头像
NAMAN RAJ15 天前

Mind blown! 🤯 That's some advanced AI magic right there! 🧙‍♂️ What do you guys think, is this the future of human-AI collaboration?

AIwithMinal 的头像
AIwithMinal15 天前

Pure artistic brilliance.

SEAR 的头像
SEAR14 天前

this is exactly the kind of learning Sear is built around

ethereagle · building 的头像
ethereagle · building15 天前

no text, video only. are those frames dumped straight into Astra's context, or is there a separate encoder in front? that's copy-with-the-API vs needing your stack.

Axel 的头像
Axel15 天前

dumped straight in

atharva ☆ 的头像
atharva ☆15 天前

woah incredible

Axel 的头像
Axel15 天前

yeah am pretty stoked

Aaron 的头像
Aaron15 天前

so cool! 😎

Axel 的头像
Axel15 天前

couldn't believe it at first tbh

Tepulous 的头像
Tepulous15 天前

$20/min .....

Axel 的头像
Axel15 天前

not if you use kv-caching

RealMan Robotics 的头像
RealMan Robotics15 天前

The shift from simulation to real-world manipulation is the part that really matters. Curious to see how far this generalization can scale across tasks and environments.

AI Quanting 的头像
AI Quanting14 天前

The control space choice is the bit Id want to see stressed. Does it switch to joint space when the demo path is actually constrained, or does it settle per task type regardless?

Hibrinix 的头像
Hibrinix14 天前

Meanwhile your future mobile phone

Amogh Shrivastava 的头像
Amogh Shrivastava14 天前

that looks peak

Axel 的头像
Axel14 天前

That's because it is

Degenpark 的头像
Degenpark14 天前

end-effector choice is huge

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