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Having humans annotate data to pre-train robots is expensive and time-consuming! Introducing SPRINT: A pre-training approach using LLMs and offline RL to equip robots w/ many language-annotated skills while minimizing human annotation effort! URL: 🧵👇

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Jesse Zhang @ ICML 2026 ✈️

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24,665 görüntüleme • 3 yıl önce •via X (Twitter)

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Jesse Zhang3 yıl önce

Labeling demonstrations with natural language instructions in hindsight is standard, but it is tedious and expensive to scale. We propose automatically (1) **relabeling** language instructions and (2) **chaining** trajectories together to generate more training data.

Jesse Zhang profil fotoğrafı
Jesse Zhang3 yıl önce

(1) Relabeling: If we have two skills, "Put mug in coffee machine" and "Press brew button," we could call this "Make Coffee." In SPRINT, we do this relabeling **automatically** by prompting an LLM to summarize nearby instructions. This gives us 2-2.5X more pre-training data!

Jesse Zhang profil fotoğrafı
Jesse Zhang3 yıl önce

(2) Chaining: Offline RL can "stitch" trajectories to learn new behaviors. We carefully relabel rewards with offline RL and modified language instructions to allow stitching even with language conditioning!

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Jesse Zhang3 yıl önce

Results Overall, this allows us to achieve 2-8X better zero-shot long horizon task execution in ALFRED, a realistic household simulator, and on a real robot setup! SPRINT agents also fine-tune more efficiently to new tasks in unseen environments! ALFRED results:

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Jesse Zhang3 yıl önce

Real Robot Results With offline fine-tuning, SPRINT achieves superior performance on new, long-horizon manipulation tasks in previously unseen environments!

Jesse Zhang profil fotoğrafı
Jesse Zhang3 yıl önce

For more details about SPRINT and experiment results, please see our paper or website. Paper: Website: Work done in collaboration with @KarlPertsch, @JiahuiZhang_32, @JosephLim_AI. @JiahuiZhang_32 is applying for PhD this year!

OliviaLi profil fotoğrafı
OliviaLi2 yıl önce

It sounds great, so that humans don't have to complete such a large amount of work every day, just let the robot do it

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