Run Teleop

OrganizationPopular

AgibotTech

run-teleop

Launch the geniesim_teleop VR / Pico teleoperation loop (or the in-process image bridge) using the `geniesim teleop` CLI verb, typically inside the Genie Sim GUI Docker container. Trigger: When the user asks to "start teleop", "run teleop", "启动遥操作", "VR 采集", "遥操作采集", "drive the robot with the VR headset", "launch the teleop loop", or wants to run anything under `geniesim_teleop`.

Overview

Publisher	AgibotTech
Repository	`genie_sim`
Skill name	`run-teleop`
Stars	1.4K
Forks	119
Bundled files	Instructions only
License	MPL-2.0
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by AgibotTech on GitHub. Read the source before you install it.

Installation

Install the Run Teleop AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/AgibotTech/genie_sim/tree/main/source/geniesim_teleop/skills/run-teleop

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/AgibotTech/genie_sim.git /tmp/genie_sim
mkdir -p .claude/skills
cp -r /tmp/genie_sim/source/geniesim_teleop/skills/run-teleop .claude/skills/run-teleop

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Run Teleop in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Run Teleop on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Run Teleop is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

When to Use

User wants to teleoperate the simulated robot with a VR device (Pico) and optionally record episodes.
User references geniesim_teleop, teleop.py, or the teleop bridge.

Do not use for:

Running a benchmark task → run-benchmark.
Verifying an inference server → check-inference.

Critical Patterns

The runtime needs ROS 2 + Isaac Sim on the host. Inside the Genie Sim Docker image (geniesim docker up → geniesim docker into) that's already set up. Outside the container, source your ROS overlay and have Isaac Sim available.
A VR device must be reachable. The teleop loop opens a VR server (default port 8080) and waits for the Pico headset to connect.
Working directory: anywhere under the repo works — the CLI uses find_spec to locate the geniesim_teleop package.
Confirm before launching. Teleop holds a GPU and a live device connection; ask before kicking it off if there's any ambiguity.

Workflow

Step 1 — Collect inputs

Ask via AskUserQuestion (all optional — sensible defaults exist):

Device type (default pico).
VR port (default 8080).
Robot config (default G2_omnipicker.json).
gRPC client host (default localhost:50051).

Step 2 — Launch the teleop loop

Inside the GUI container (geniesim docker into):

bash
geniesim teleop run --device_type=pico --port=8080

With explicit overrides:

bash
geniesim teleop run \
    --client_host=localhost:50051 \
    --port=8080 \
    --robot_cfg=G2_omnipicker.json \
    --device_type=pico

Step 3 — (optional) Image bridge

If the user needs the in-process image pub/sub bridge:

bash
geniesim teleop bridge --mode inprocess

Commands (copy-paste summary for the user)

bash
# Terminal A — host (start GUI container)
cd /path/to/main
./scripts/start_gui.sh

# Terminal B — host, then container
cd /path/to/main
./scripts/into.sh
# inside container:
geniesim teleop run --device_type=pico --port=8080

Notes

If geniesim isn't on $PATH, substitute python3 -m geniesim_cli teleop … — same args.
Any unknown --flag after the subcommand is forwarded verbatim to geniesim_teleop.teleop / geniesim_teleop.bridge.
Robot init states are loaded from the geniesim_benchmark package when it's installed; if it isn't, teleop still runs (init-state loading is skipped with a warning).

Frequently asked questions

What does the Run Teleop AI skill do?

Why use Run Teleop on TypingMind?

Because you install it once and use it with any model. Run Teleop is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Run Teleop in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AgibotTech/genie_sim/tree/main/source/geniesim_teleop/skills/run-teleop. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Run Teleop?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Run Teleop?

As many as you like. As long as a model supports skills, you can use Run Teleop with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Run Teleop AI skill free?

Yes. It is published on GitHub by AgibotTech under the MPL-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Receiving Code Review

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Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative…

Requesting Code Review

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Use when completing tasks, implementing major features, or before merging to verify work meets requirements

Systematic Debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Test Driven Development

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Use when implementing any feature or bugfix, before writing implementation code

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

When to Use

Critical Patterns

Workflow

Step 1 — Collect inputs

Step 2 — Launch the teleop loop

Step 3 — (optional) Image bridge

Commands (copy-paste summary for the user)

Notes

Frequently asked questions

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