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Humans learn by watching. Robots should too.

1,078,254 次观看 • 9 个月前 •via X (Twitter)

37 条评论

Skild AI 的头像
Skild AI9 个月前

Unfortunately, cooking is not our talent. So we teach our robots to cook for us.

Skild AI 的头像
Skild AI9 个月前

See our robot open doors, water plants, assemble a box, and more by learning from watching humans. Using <1hr of robot data.

Skild AI 的头像
Skild AI9 个月前

Skild Brain is robust to adversarial disturbances and transfers zero-shot to unseen homes.

Skild AI 的头像
Skild AI9 个月前

Skild Brain is omni-bodied. Any robot, any task. See it load a dishwasher using a different form-factor.

Skild AI 的头像
Skild AI9 个月前

Ever lost your AirPods? Here, a different embodiment assembles 10+ AirPods cases in a row, requiring extreme precision and dexterity.

Skild AI 的头像
Skild AI9 个月前

Skild Brain is highly adaptable and reasons via in-context learning.

Skild AI 的头像
Skild AI9 个月前

Blog → Careers → Follow @SkildAI for updates.

Josh Machiz 的头像
Josh Machiz9 个月前

we live in the future

Skild AI 的头像
Skild AI9 个月前

Indeed, grateful for @lightspeedvp's continued support!!

Josh Machiz 的头像
Josh Machiz9 个月前

@lightspeedvp 🫡

Bercan 的头像
Bercan9 个月前

🥳🥳🥳🥳🥳🥳🥳

Felicis 的头像
Felicis9 个月前

🦾🧡

Vibhor Khanna 的头像
Vibhor Khanna9 个月前

This is an insane breakthrough - huge congrats to the Skild team

Jeffeson Rocha 的头像
Jeffeson Rocha7 个月前

@GMNDProtocol is building the eyes that validate what they watch

Oli 的头像
Oli9 个月前

cool but how about non static tasks that require the robot to walk around aswell

EgoScale 的头像
EgoScale9 个月前

Long-horizon generalization is less about clever architectures, and more about seeing enough diverse, real-world behavior over time.

Himanshu Kumar 的头像
Himanshu Kumar9 个月前

@SkildAI, that's a concise point, and imitation learning is a great way to train robots, indeed.

mmurph 的头像
mmurph9 个月前

amazing object recognition and fluidity of motion... even under duress!

Genesis Robotics Network 的头像
Genesis Robotics Network9 个月前

Yes! And the next step is making that demonstration data reusable and accessible. One company’s teleoperation sessions = training data for the whole ecosystem. That’s how you scale imitation learning beyond single labs.

Lyn 的头像
Lyn9 个月前

Would love to see examples of it learning purely from watching without any preset motions

Tony Lu 的头像
Tony Lu6 个月前

That's cool! If robots can really learn by watching, then the bottleneck to robotics shifts from hard-coded control to data and imitation. Deployment can get much cheaper

福爸说人形机器人(J.W) 的头像
福爸说人形机器人(J.W)9 个月前

how do you compare with FSD, as both being Video learner?

Praveen Kumar 的头像
Praveen Kumar9 个月前

Does this mean the act of collecting egocentricdata from Asian countries is dead???

Mentis 🇦🇺 的头像
Mentis 🇦🇺9 个月前

The gap between watching a video and actually feeling the torque is still huge. Interested to see how they bridge it.

Ofir Ozeri 的头像
Ofir Ozeri9 个月前

I wonder how old are this abilities?

Dhruv Diddi 的头像
Dhruv Diddi9 个月前

Nice! We got VLAs now 🦾😎💯

WINNER⚖️ 的头像
WINNER⚖️9 个月前

Soothing watching 👀

Rohan Mehta 的头像
Rohan Mehta3 个月前

watch and learn

Joe Crescenzi - AskJoe.TV 的头像
Joe Crescenzi - AskJoe.TV9 个月前

This is exactly why the best robotic designs use a humanoid design. It's much easier to teach a robot to do tasks when they can simply watch us and mirror what they see, limb or limb, joint for joint.

Mateo Hernandez 的头像
Mateo Hernandez9 个月前

The birth of AI and robotics, along side God and Man.

Tim Li 的头像
Tim Li6 个月前

great

fuckoff 的头像
fuckoff9 个月前

the cheeks on that robot

Marius78.crypto ױ / 🤖ボッ 的头像
Marius78.crypto ױ / 🤖ボッ9 个月前

Progress is advancing / @xmaquina 👀

Ankit Khandelwal 的头像
Ankit Khandelwal9 个月前

Learning from watching and then trial &amp; error is definitely is the most scalable way forward that will make the most significant difference.

jklre 的头像
jklre9 个月前

If a robot touches a hot stove what happens?

Skild AI 的头像
Skild AI9 个月前

stove becomes cold?

jklre 的头像
jklre9 个月前

before or after the robot melts or gets damaged?

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