Bingao Chen (@bingao_chen)

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Wrist cameras give robots close-up views, but only where the hand happens to be.
What if the policy could decide where to look?

Introducing FoveaVLA: a VLA that replaces the wrist camera with learned foveation actions.

FoveaVLA predicts where it will need to look next, then crops that region from the full-resolution external image. The full view gives context; the foveal crop gives close-up detail.

With one external camera and no wrist camera, FoveaVLA reaches 70.8% success on 48 real-world trials, vs. 52.1% for π0.5 with a wrist camera.

• No additional demonstrations
• No camera calibration
• No wrist camera

Paper, code, and models are all open 🧵

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Bingao Chen @bingao_chen Oct 25

When I tried using Astra to tackle RoboMME a month ago, the results were still below human performance. Now the benchmark has almost been solved. Great work @liu_yuejiang !

Yuejiang Liu @liu_yuejiang Oct 25

What should a robot remember? Can it learn to remember better than we tell it to?

Excited to share MEMI: Reverse-Engineering What Robots Should Remember

📈 97.0% end-to-end, 99.4% w/ low-level controller on RoboMME, nearly solving it for the first time

🧵👇

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Bingao Chen @bingao_chen Sep 23

Really like the idea of distilling coding-agent solutions into VLAs. Leveraging the capabilities of frontier models while amortizing their latency and cost feels essential for practical robot learning.

Haoxiang You Verified account @haoxiang_you Sep 23

How far can coding agents go in the physical world — and can what they discover be distilled into fast System 1 robot policies like VLAs?

Introducing EmbodiedSWE, where we systematically study frontier agents across a wide range of precise, dexterous, and long-horizon robot tasks. The results surprised us: these agents could already autonomously solve remarkably complex physical tasks.

But solving is only the beginning. Running a coding agent every time a robot acts is expensive, slow, and difficult to deploy safely in the real world.

So we asked: can we turn what coding agents discover into fast robot policies?

We explore one direction: using coding-agent solutions as teachers for VLAs. A single successful solution can be expanded into a large and diverse training dataset — at surprisingly low cost.

This suggests a compelling path toward general-purpose robots: a GPT-like “brain” for reasoning and discovering solutions, paired with a fast VLA or RL “cerebellum” for acting in the physical world.

For the first time, everyday general-purpose robots don’t feel quite so far away.

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Bingao Chen @bingao_chen Sep 21

Can Astra Solve RoboMME without Breaking the Bank? 🤖

79.13% success—with just 3.63 Astra calls per episode.

Remember a hidden object. Follow a sequence demonstrated in a video. Count how many times an action has been completed. RoboMME challenges robots to act on information beyond the current camera view.

We built a three-tier system that pairs Astra’s reasoning with a small visual monitor and a VLA. The key: let Astra decide what to do, and let the small model decide when to ask it again.

Evaluated on 800 official test episodes across 16 tasks.

Here’s how it works—and what we found. 🧵👇

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Bingao Chen @bingao_chen Sep 9

Congrats to @YeZhangchen0731 and the whole team!

Runhan Huang ✈️ CoRL 2026 @RunhanH Sep 9

Excited to share our work accepted to CoRL 2026 @corl_conf !

We present SkillX: Unified Multi-Skill Policy Learning for Humanoid Soccer — a unified policy that enables humanoid robots to learn and compose diverse soccer skills, achieving smooth skill transitions and long-horizon multi-skill behaviors.

Project page: [SkillX project page](yzc0731.github.io/SkillX/)
Paper: [SkillX: Unified Multi-Skill Policy Learning for Humanoid Soccer (arXiv)](arxiv.org/abs/2609.06718)

Video below 👇

#CoRL2026 #RobotLearning #Robotics #AI #EmbodiedAI #HumanoidRobots #Soccer

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Bingao Chen

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