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TidyBot: Personalized Robot Assistance with Large Language Models approach enables fast adaptation and achieves 91.2% accuracy on unseen objects in our benchmark dataset. We also demonstrate our approach on a real-world mobile manipulator called TidyBot, which successfully puts away 85.0% of objects in real-world test scenarios abs: project page:... show more

AK

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326,009 görüntüleme • 3 yıl önce •via X (Twitter)

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Jimmy Wu profil fotoğrafı

Jimmy Wu3 yıl önce

Thanks @_akhaliq for sharing our work! I wrote a thread with more details here:

hardmaru profil fotoğrafı

hardmaru3 yıl önce

This is the easy part :) I need a robot that can clean the bits of pieces of jam, bread crumbs, diapers, and occasionally pieces of poo around various corners of the room, under the tables, and hidden in the kids play area of the house.

hgtp:// Alkimi $ADS $QNT Cat 🐈‍⬛ profil fotoğrafı

hgtp:// Alkimi $ADS $QNT Cat 🐈‍⬛3 yıl önce

How do I order one??

Defend Intelligence (Anis Ayari) profil fotoğrafı

Defend Intelligence (Anis Ayari)3 yıl önce

Really nice ! thank you for demonstrating this capability. LLM could then indeed be used as the "reasoning" block to achieves unseen world reasonning and allow tasks to get a better generalization to accomply them.

Dan Rockwell profil fotoğrafı

Dan Rockwell3 yıl önce

I felt like it was missing something..

St. Clair Newbern IV profil fotoğrafı

St. Clair Newbern IV3 yıl önce

Should be the standard upsell on all children. 😂

Astral Turf profil fotoğrafı

Astral Turf3 yıl önce

@ericjang11 Not very impressive really.

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