# Build a Reachy Mini MuJoCo simulation on Raspberry Pi

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/cross-platform/build-a-reachy-robot-app-on-pi/)
- [Learn about Reachy and the Reachy Gladiator application](https://learn.arm.com/learning-paths/cross-platform/build-a-reachy-robot-app-on-pi/reachy-app/)
- [Start the Reachy simulation on MuJoCo](https://learn.arm.com/learning-paths/cross-platform/build-a-reachy-robot-app-on-pi/start-simulation/)
- [Set up the Raspberry Pi and run the edge AI app](https://learn.arm.com/learning-paths/cross-platform/build-a-reachy-robot-app-on-pi/run-pi-app/)
- [Use the Reachy Gladiator application](https://learn.arm.com/learning-paths/cross-platform/build-a-reachy-robot-app-on-pi/use-the-app/)
- [Understand the Reachy Gladiator application code](https://learn.arm.com/learning-paths/cross-platform/build-a-reachy-robot-app-on-pi/understand-code/)
- [(Optional) Extend the project](https://learn.arm.com/learning-paths/cross-platform/build-a-reachy-robot-app-on-pi/extend/)
- [Next Steps](https://learn.arm.com/learning-paths/cross-platform/build-a-reachy-robot-app-on-pi/_next-steps/)

## About this Learning Path

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| Skill level: | Introductory |
| Reading time: | 1 hr |
| Last updated: | 12 Nov 2026 |

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| Author: | Matt Cossins , Arm<br>[GitHub](https://github.com/matt-cossins) |
| Arm IP: | [Cortex-A](https://support.arm.com/?tab=compute-ip&Product%20Type=Application%20Processors) |
| Tags: | [AI/ML](https://learn.arm.com/tag/ai-ml/) [Linux](https://learn.arm.com/tag/linux) [macOS](https://learn.arm.com/tag/macos) [Windows](https://learn.arm.com/tag/windows) [Raspberry Pi](https://learn.arm.com/tag/raspberry-pi) [Reachy Mini](https://huggingface.co/reachy-mini) [Python](https://learn.arm.com/tag/python) [MediaPipe](https://learn.arm.com/tag/mediapipe) [FastAPI](https://learn.arm.com/tag/fastapi) [MuJoCo](https://learn.arm.com/tag/mujoco) |

## Who is this for?
This Learning Path is for developers interested in edge AI, robotics simulation, and physical AI applications. You can complete the main path without owning a physical Reachy Mini.

## What will you learn?
After completing this Learning Path, you'll be able to perform the following tasks:
- Understand why simulation environments can aid Edge AI and robotics development.
- Run a simulated Reachy Mini robot on a laptop or desktop.
- Use MediaPipe and TensorFlow Lite gesture recognition on Raspberry Pi 5.
- Connect an edge inference node to a robot daemon over the network.
- Display results over a web dashboard.
- Optionally extend the project toward a physical Reachy Mini, audio or multimodal interaction, or your own app.

## Prerequisites
Before starting, you'll need the following prerequisites:
- A Raspberry Pi 5, ideally with 16 GB RAM.
- A USB webcam connected to the Raspberry Pi.
- A macOS or Linux machine, or a Windows machine with WSL2, capable of running the Reachy Mini MuJoCo simulation.
- Basic Python and Bash terminal experience.
- (Optional) [Reachy Mini](https://huggingface.co/reachy-mini)

## Summary
You’ll build a distributed edge AI application that runs MediaPipe gesture inference on a Raspberry Pi 5 and drives a Reachy Mini robot in a MuJoCo simulation. First, you’ll start the simulator on a development machine, prepare the Pi with Raspberry Pi OS and Git LFS so the gesture model downloads correctly, then run the app and open a browser dashboard to monitor the camera feed and verdicts. A thumbs-up or thumbs-down from the webcam triggers victory or defeat motions in the simulated robot. You’ll also learn about the project layout so you can locate perception, app logic, motion, and dashboard components in the codebase to iterate on.