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Robot AI brains, aka Vision-Language-Action models, cannot adapt to new tasks as easily as LLMs like Gemini, ChatGPT, or Grok. LLMs can adapt quickly with their in-context learning (ICL) capabilities. But can we inject ICL abilities into a pre-trained VLA like pi0? Yes! Introducing RICL (Retraining for In-Context Learning),... show more

Kaustubh Sridhar

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52,897 просмотров • 1 год назад •via X (Twitter)

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Комментарии: 9

Фото профиля Kaustubh Sridhar

Kaustubh Sridhar1 год назад

How does it work? RICL-pi0 is given access to a few demos in new/long-tail tasks. It never finetunes on these demos… only retrieves relevant bits to throw into its context in each timestep. We trained it on a few “priming” tasks, with RAG during training, so that it learns to use its retrieved context effectively. We then evaluate on unseen/long-tail tasks. A video with comparisons of pi0 and RICL-pi0:

Фото профиля Kaustubh Sridhar

Kaustubh Sridhar1 год назад

“Finetuning like you pretrain”: If you finetune on target task data exactly like RICL was pretrained on the priming tasks, you get the best performing policies. The below video shows the reactivity and robustness of RICL-pi0-finetuned policies in various target tasks, under human perturbations

Фото профиля Kaustubh Sridhar

Kaustubh Sridhar1 год назад

TLDR: RICL-pi0-finetuned >> pi0-finetuned ≈ RICL-pi0 (only RAG + ICL) >> pi0 (out-of-the-box) Please find more information about RICL (including many limitations) at website: and arxiv: This work was my last PhD paper before joining @GoogleDeepmind and would not have been possible without @1_souradeep, @dineshjayaraman, and @insuplee. RICL has been accepted to CoRL 2025 in Seoul, Korea. If you’re there, @1_souradeep will be happy to tell you all about our work.

Фото профиля Kaustubh Sridhar

Kaustubh Sridhar1 год назад

FYI, this work builds on our previous work REGENT (qt’ed below):

Фото профиля PrismaX

PrismaX1 год назад

Pi0 unlocked the promptability upgrade! Let's give it to 1,000 tele-op users and see how robots learn with real-world feedback.

Фото профиля Mykhailo Sorochuk

Mykhailo Sorochuk1 год назад

Impressive approach. RAG + ICL for VLA? That could really push robot adaptability. Curious how well it generalizes without fine-tuning.

Фото профиля Aadarsh R

Aadarsh R1 год назад

ICL can unlock test-time diverse generalization. Very interesting direction! Nice to see Idli plate in the experiments :)

Фото профиля Abhranil Chandra @ RLC 2026 🇨🇦

Abhranil Chandra @ RLC 2026 🇨🇦1 год назад

Great work @_k_sridhar. Both this and REGENT, seem like an interesting direction for fast adaptation in FMs for embodied tasks...

Фото профиля KillerBambi2

KillerBambi21 год назад

AI brain is not the same as a human brain

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🚀🚀🚀 Ever wondered what it takes for robots to handle real-world household tasks? long-horizon execution, deformable object dexterity, and unseen object generalization — meet GR-3, ByteDance Seed’s new Vision-Language-Action (VLA) model! GR-3 is a generalizable Vision-Language-Action (VLA) model with strong capabilities in complex long-horizon tasks. It understands unseen abstract concepts, manipulates deformable objects robustly, and adapts to novel settings with minimal human data. ✨ Generalization: Generalizes well to unseen objects, environments, and even instructions with abstract concepts. ✨ Long-Horizon Manipulation: Completes long-horizon tasks with strong instruction-following capabilities. ✨ Deformable Object Manipulation: Manipulate deformable objects robustly. Project Page: Arxiv: #ByteDance #ByteDanceSeed #GR3 #VLA #Robotics #FoundationModels

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