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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

506,562 subscribers

326,009 Aufrufe • vor 3 Jahren •via X (Twitter)

Bildung Wissenschaft & Technologie Nachrichten & Politik

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7 Kommentare

Profilbild von Jimmy Wu

Jimmy Wuvor 3 Jahren

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

Profilbild von hardmaru

hardmaruvor 3 Jahren

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.

Profilbild von hgtp:// Alkimi $ADS $QNT Cat 🐈‍⬛

hgtp:// Alkimi $ADS $QNT Cat 🐈‍⬛vor 3 Jahren

How do I order one??

Profilbild von Defend Intelligence (Anis Ayari)

Defend Intelligence (Anis Ayari)vor 3 Jahren

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.

Profilbild von Dan Rockwell

Dan Rockwellvor 3 Jahren

I felt like it was missing something..

Profilbild von St. Clair Newbern IV

St. Clair Newbern IVvor 3 Jahren

Should be the standard upsell on all children. 😂

Profilbild von Astral Turf

Astral Turfvor 3 Jahren

@ericjang11 Not very impressive really.

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