# Bevy MuJoCo [![Crates.io](https://img.shields.io/crates/v/bevy_mujoco.svg)](https://crates.io/crates/bevy_mujoco) [![MIT/Apache 2.0](https://img.shields.io/badge/license-MIT%2FApache-blue.svg)](https://github.com/bevyengine/bevy#license) [![Crates.io](https://img.shields.io/crates/d/bevy_mujoco.svg)](https://crates.io/crates/bevy_mujoco) [![Rust](https://github.com/stillonearth/bevy_mujoco/workflows/CI/badge.svg)](https://github.com/stillonearth/bevy_mujoco/actions) https://user-images.githubusercontent.com/97428129/210613348-82a5e59d-96af-42a9-a94a-c47093eb8297.mp4 Import MJCF files into Bevy and run simulations with MuJoCo. ## Implementation Notes MuJoCo has 2 modes with different coordinate systems for bodies 1. `paused` mode where all translations and rotations are extracted from `mj_Model` in `MuJoCo-Rust` as `body.pos`, `body.quat` in parent's body coordinate system. To make them work nice with bevy the body structure from mujoco has to be transformed to a tree structure with `body_tree()` call. Then `body_tree` is spawned into the bevy world recursively — a nice contraption to do it in `setup_mujoco`. 2. `simulation` mode where translations are extracted from `sim.xpos()` and `sim.xquat()` — and this time they are in global frame. Since bodies are spawned hierarchically translations and rotations need to be converted to a parent coordinate system — it happens in `simulate_physics`. ## Getting Started ### MuJoCo Dependency - `MuJoCo` 2.3.5 installed in `~/.local/mujoco` for Linux or `C:/Program Files/Mujoco` for Windows - _nightly_ Rust. Compile with `cargo +nightly build` ### Usage ```rust // 1. Import bevy_mujoco use bevy_mujoco::*; // 2. Setup bevy_mujoco Plugin. MuJoCo Plugin would spawn entities to the world fn main() { App::new() .add_plugins(DefaultPlugins) .insert_resource(MuJoCoPluginSettings { model_xml_path: "assets/unitree_a1/scene.xml".to_string(), pause_simulation: false, target_fps: 600.0, // this is not actual fps (bug in bevy_mujoco), // the bigger the value, the slower the simulation }) .add_plugins(MuJoCoPlugin) .add_systems(Startup, setup) .add_systems(Update, robot_control_loop) .run(); } // 3. You can control your robots here fn robot_control_loop(mut mujoco_resources: ResMut) { // prepare simulation data for the NN let qpos = mujoco_resources.state.qpos.clone(); let qvel = mujoco_resources.state.qvel.clone(); let cfrc_ext = mujoco_resources.state.cfrc_ext.clone(); // Compute input -> control values here and fill control // ... let mut control: Vec = Vec::new(); mujoco_resources.control.data = input_vec; } ``` **copy build.rs to root of your project to use in with Windows environments. it will copy mujoco.dll to a build dir of your application** To run tests and example initialize [`mujoco_menagerie`](https://github.com/deepmind/mujoco_menagerie) submobule with ```bash cd bevy_mujoco git submodule init git submodule update ``` See [example](https://github.com/stillonearth/bevy_quadruped_neural_control) for simulating Unitree A1 robot.