Embodify

Name: Embodify
Author: YidaYang

作者：YidaYang版本 0.1.0a2i★ –i在你的电脑上运行

注册名称io.github.YidaYang/embodify

安装

概览

作者提供的说明（英文原文）

Give your agent a body: observe and control simulated robots (LIBERO, RoboDojo) over MCP.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 其他

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
注册中心没有列出需要填写的密钥。

你需要

PyPI已安装 Python 的 uv（用于 uvx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "embodify": {
      "command": "uvx",
      "args": [
        "embodify-mcp"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add embodify -- uvx embodify-mcp

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "embodify": {
      "command": "uvx",
      "args": [
        "embodify-mcp"
      ]
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "embodify": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "embodify-mcp"
      ]
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 YidaYang 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“其他”中的其他服务器比较i

星标数多于此分类中其他 32531 个服务器的 0%
发布到注册中心的时间比此分类中其他 32531 个服务器的 53% 更新
基础检查结果等于或优于此分类中其他 32531 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Embodify 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.1.0a2）于35天前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它？

同一分类中有：Spec Driven Development, Remote Desktop Commander, Reelsaw

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Spec Driven Development

作者：formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

在你的电脑上运行★ 440MIT14个月前更新

安装

Remote Desktop Commander

作者：desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

在云端运行★ 211昨天更新

安装

Reelsaw

作者：osbytes

Drive a self-hosted Reelsaw: turn long videos into vertical clips, edit, re-render and export them.

在你的电脑上运行今天更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

Embodify

Give your agent a body.

Your best embodied agent is your favorite agent.

Embodify lets the agent you use every day — Claude Code, Codex or any other MCP-capable agent — see and control robots directly, and keeps everything else that makes it yours. Bring in frontier models with embodied manipulation skills, such as GPT-6 Astra and Claude Opus 5.5, and let your AI companion step into the physical world.

中文 · One-line setup · Backends · For research · Skills · Roadmap

First release (0.1.0a1). Supports LIBERO and RoboDojo today; support for

RoboTwin and the LeRobot SO-101 arm is coming soon.

Why Embodify

Most embodied agents are built from scratch: a dedicated harness wraps a model, hands it a fixed set of robot actions, and nothing else. The agent you use every day already has what those harnesses lack:

Context and memory. It manages long sessions and remembers across them.
It knows you. Your preferences, your projects, your lab setup.
It talks with you in the terminal, IDE, desktop app or chat you already use. You can ask how it is going, step in, correct it, or teach it mid-task.
It has a computer. It writes and runs code, uses its tools, searches the web and reads papers.

Embodify keeps all of that and adds a body. Install the plugin and your agent gets robot observation and control tools over MCP — a protocol it already uses for everything else — plus skills that teach it how to operate robots and how to achieve recursive self-improvement (RSI) from its own experience.

Why "best"

A general agent with a body is stronger than a harness that can only move a robot:

It can think with tools, not only act. When a task needs geometry it can write a script; when it needs a fact it can search; when it needs perception it can run a model.
It keeps track. Long-horizon manipulation fails when the agent forgets what it already tried. Mature agents manage context and memory well.
It works with you. It asks you when a task is ambiguous, takes your guidance when it gets stuck, and remembers your corrections next time.
It controls robots in its native language. Robot control arrives as ordinary MCP tool calls, the same shape as every other tool the agent uses.
It builds on frontier embodied models. Your agent runs on models with frontier embodied manipulation skills, such as GPT-6 Astra and Claude Opus 5.5, and every model upgrade makes your robot better, with no retraining.
It improves itself recursively. It turns every episode into lessons, lessons into rules and rules into new skills, and gets better the more it works.
Zero-shot, few-shot and in-context learning come easily. A general agent takes on new tasks without training: describe a task in plain language (zero-shot), show it a few examples (few-shot), or put instructions, demonstrations and past experience in its context (in-context learning, ICL).

What's inside

Embodify is an agent plugin with two parts, and installing the plugin gives your agent both:

Part	What it gives your agent
MCP server (`embodify-mcp`, registered as `embodify`)	Tools to list tasks, start an episode, observe cameras and robot state, move end effectors, open and close grippers, and coordinate two arms.
Skills	Know-how: running a careful observe–act loop, keeping a profile of the robot body and cameras, and learning from past episodes. More perception skills are coming soon.

flowchart LR

A["Your agent Claude Code · Codex · …"] -- "MCP (stdio)" --> B["embodify-mcp episodes · budgets · logs"] K["Embodify skills"] -. "loaded by" .-> A B --> I["Backend interface"] I --> L["LIBERO"] I --> R["RoboDojo"] I --> T["RoboTwin (WIP)"] I --> H["SO-101 and other real robots (WIP)"] I -. "SSH / TCP" .-> G["Remote server"]

The MCP server is the front end. Each simulator, benchmark or robot is a backend behind one small interface, so adding a new one does not change what the agent sees. Highlights:

Works with any MCP host. No LLM calls inside; your agent keeps its own model, memory, skills and tools.
One call, one motion. The control loop runs next to the simulator. Each action returns the actual displacement, remaining error, a stop reason and fresh camera images.
Remote simulation. Run the simulator on your lab's GPU server over SSH while the agent stays on your laptop. Heartbeats keep high-latency links alive; if the link drops, the episode aborts cleanly and reset_task reconnects.
Fair evaluation. When you use Embodify to evaluate an agent's manipulation skills, task success is recorded for humans and never exposed to the agent.
Live monitor and replay. Watch running episodes live in a browser, or replay past ones frame by frame.

Backends

Backend	Robot	Status
`fake`, `fake-two-arm`	Kinematic diagnostic, one or two arms	✅ Included, no simulator needed
`libero`	Franka Panda, 130 tasks in 5 suites	✅ Included
`robodojo`	Dual ARX X5, all 54 simulation tasks	✅ Included
RoboTwin	Dual-arm manipulation benchmark	🚧 Coming soon
LeRobot SO-101	Real robot: 5-DoF arm with gripper	🚧 Coming soon
`remote`	Any backend above, on another machine	✅ SSH or trusted TCP

Coming soon: robot arms such as xArm, mobile manipulators, humanoids and dexterous hands. If there is a simulator or robot you would like us to support, open a GitHub issue with details about it. We welcome contributions of new backends.

For research

Embodify doubles as a testbed for the embodied manipulation abilities of language models and agent frameworks. Run GPT, Claude, Gemini, Qwen and other models, through Claude Code, Codex or your own agent framework, on the same simulated benchmarks with the same tools, and compare them directly.

Standard benchmarks: LIBERO (130 tasks in 5 suites) and RoboDojo (54 tasks), with RoboTwin coming soon.
Fair by design: task success is recorded for evaluators and never shown to the agent, and --lock-task fixes the scene.
Controlled budgets: episode budgets and per-call step limits are configurable and logged.
Complete records: tool calls, robot states and camera frames are saved for replay and analysis.

Typical studies include zero-shot robot manipulation by frontier models, comparisons of agent frameworks on the same model, the effect of skills and memory, and recursive self-improvement across episodes. See citation for how to cite Embodify and what to report.

Quick start

One-line setup

Paste this into Claude Code, Codex or any other agent you use:

Install Embodify (https://github.com/YidaYang/embodify) for yourself, then set up a LIBERO environment or connect to my existing simulator.

That's it. Your agent installs the Embodify plugin, sets up the simulator and tells you when to restart the session. (Agents: follow the agent setup guide.)

Manual install

Embodify starts its MCP server with uv, straight from PyPI. If you do not have uv yet, install it with python -m pip install uv or the official installer.

Claude Code

claude plugin marketplace add YidaYang/embodify

claude plugin install embodify@embodify

Codex

codex plugin marketplace add YidaYang/embodify

codex plugin add embodify@embodify

The plugin brings both the MCP server, registered as embodify, and the skills. The server starts on the Fake backend, so you can try the tools right away; connect a real simulator next. It reads its settings from ~/.embodify/config.json (see backend setup). To check the package on its own, without an agent:

uvx --from embodify-mcp embodify-mcp-smoke   # end-to-end check: no simulator, GPU or model needed

Other agents

Register the server in your agent's MCP configuration using examples/mcp.json.
If your agent supports Agent Skills (SKILL.md folders), copy or link the folders in plugin/skills/ into its skills directory.

Restart the session, then ask your agent something like *"Reset the task, describe what the cameras show, then raise the gripper 5 cm."*

Connect a real simulator

Let your agent do this step. Paste:

Connect Embodify to a real simulator for me: set up LIBERO on this machine, or connect to my existing simulator. Follow https://github.com/YidaYang/embodify/blob/main/docs/agent-setup.md.

Your agent installs the simulator or connects to yours over SSH, writes the server settings to ~/.embodify/config.json and asks you to restart the session. To do it by hand, see backend setup.

Watch robots live and replay episodes

Add "monitor-port": 8765 to ~/.embodify/config.json and open http://127.0.0.1:8765 to watch the robot work live, with every camera view, the robot state and each tool call your agent makes, or to replay any past episode frame by frame. To browse saved runs without a running server:

uvx --from embodify-mcp embodify-mcp-monitor   # reads ~/.embodify/runs

The monitor listens on localhost only.

MCP tools

Tool	Purpose
`get_session_info`	Current run, task, step budget and robot state; no images
`list_tasks`	Browse the backend's task catalogue
`reset_task`	Start an episode and return the first camera images
`observe`	Camera images and robot state, without moving
`move_relative`	Move one end effector by a translation and optional rotation
`set_gripper`	Open or close a gripper
`control_arms`	Move several arms together with common progress (two-arm backends)
`stop_episode`	End the episode and write its record

Translations are in meters, rotations in radians, quaternions in xyzw order. Frames and stop reasons are defined in the action contract.

Skills

The plugin contains these skills:

Skill	Status	What it does
embodied-control	v0	The observe–act loop: small moves, reading stop reasons, verifying grasps, releasing in a separate call
robot-profile	v0	Keep a profile of the robot: arms, cameras, frames, calibration and measured behavior
task-experience	v0	Write a lesson after every episode, read relevant lessons before the next, promote repeated lessons to rules
object-segmentation	Coming soon	Open-vocabulary segmentation of camera images
depth-ranging	Coming soon	Pixel to 3D position from depth and calibration

Skills are grouped into three families that will keep growing:

Embodiment knowledge: how this particular robot is built, where its cameras are and how it actually moves.
Manipulation tools: perception, measurement and planning tools, including ideas from work such as Code as Policies.
Recursive self-improvement: turning experience into lessons, lessons into rules and eventually into new skills, improving recursively.

Roadmap

Backends: RoboTwin; LeRobot SO-101 as the first real robot, with workspace limits, human-judged success and an emergency stop;

只显示了开头部分；其余内容请在仓库中阅读

Embodify

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“其他”中的其他服务器比较i

常见问题

替代选择

Spec Driven Development

Remote Desktop Commander

Reelsaw

作者的 README

Why Embodify

Why "best"

What's inside

Backends

For research

Quick start

One-line setup

Manual install

Claude Code

Codex

Other agents

Connect a real simulator

Watch robots live and replay episodes

MCP tools

Skills

Roadmap