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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 views • 3 years ago •via X (Twitter)

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Jimmy Wu3 years ago

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

hardmaru3 years ago

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 🐈‍⬛3 years ago

How do I order one??

Defend Intelligence (Anis Ayari)3 years ago

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 Rockwell3 years ago

I felt like it was missing something..

St. Clair Newbern IV3 years ago

Should be the standard upsell on all children. 😂

Astral Turf3 years ago

@ericjang11 Not very impressive really.

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