@svpino: If you connect this to your coding agent, you can control a simulated robot and build experiences you can reuse in othe…

X AI KOLs Following Papers

Summary

EmbodiedEvo 让编码代理在模拟机器人场景中编写 Python 代码、执行任务并将经验固化为可复用的技能,从而零样本迁移到真实机器人与新环境,无需更新模型权重。

If you connect this to your coding agent, you can control a simulated robot and build experiences you can reuse in other scenes. They have an example using the Codex CLI. • Set it up with your coding agent • Give your robot a task • Your coding agent writes Python code to control the scene • It executes the code and checks what happened • It saves reusable procedures as skills You can then reuse those lessons with a real robot in a different environment. No need to retrain a model.
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If you connect this to your coding agent, you can control a simulated robot and build experiences you can reuse in other scenes.

They have an example using the Codex CLI.

• Set it up with your coding agent • Give your robot a task • Your coding agent writes Python code to control the scene • It executes the code and checks what happened • It saves reusable procedures as skills

You can then reuse those lessons with a real robot in a different environment. No need to retrain a model.

Einsia (@EinsiaAI): Coding agents are entering the physical world. Zero-shot is only the starting point. What if we gave them practice—and let the next agent inherit what they learned?

EmbodiedEvo: one Playground → reusable Skills & Lessons → unseen layouts. No model-weight updates.

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X AI KOLs Following

Current mainstream pure data-driven robot solutions suffer from low data efficiency and poor generalization. The newly proposed neuro-symbolic physical intelligence paradigm breaks down tasks into two steps: world modeling and planning. It requires only 1-10 demonstrations to learn new tasks, and its generalization ability far exceeds traditional end-to-end solutions, providing a more reliable path for general-purpose robots.