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How can we help *any* image-input policy generalize better? 👉 Meet PEEK 🤖 — a framework that uses VLMs to decide *where* to look and *what* to do, so downstream policies — from ACT, 3D-DA, or even π₀ — generalize more effectively! 🧵

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Jesse Zhang

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13,825 Aufrufe • vor 11 Monaten •via X (Twitter)

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Jesse Zhangvor 11 Monaten

By offloading high-level reasoning of "what" and "where" to VLMs, PEEK lets robot policies focus on learning "how" to act. It’s a step toward zero-shot generalization in manipulation — giving robots a “peek” at the world.

Profilbild von Jesse Zhang
Jesse Zhangvor 11 Monaten

Our policy training data consists of: - simple sim envs for 3DDA (3D and RGB input) - BRIDGE-v2 (for π₀ and ACT) This data is labeled with the PEEK VLM to directly draw “where” to focus and “what” to do onto the image.

Profilbild von Jesse Zhang
Jesse Zhangvor 11 Monaten

We fine-tune policies on PEEK-VLM labeled data. We test zero-shot. 💥 41.4× sim2real improvement (3DDA) 💥 2–3.5× boosts for π₀ and ACT PEEK gives visuomotor policies the minimal cues to generalize across new tasks, scenes, and semantics.

Profilbild von Jesse Zhang
Jesse Zhangvor 11 Monaten

📄 Paper: 💻 Demo: 🔗 Website, code, data, models: Work done w/ @memmelma (co-lead), @minjunkevink, @fox_dieter17849 , @_jessethomason_ , Fabio Ramos, @ebiyik_ , @abhishekunique7 , @AnqiLi24

Profilbild von Jesse Zhang
Jesse Zhangvor 11 Monaten

Finally, see @memmelma's tweet thread to learn how the method works!

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