# Locomotion robots

The bundled locomotion presets use the generic `Robot` runtime. Each model has
its own page with an individual render, usage example and configuration summary.
They can be selected independently of a task through the robot catalog:

| Catalog ID | Configuration class | Controlled joints |
| --- | --- | --- |
| {doc}`UnitreeGo1 </resources/robot/unitree/go1>` | `UnitreeGo1Cfg` | 12 |
| {doc}`UnitreeGo2 </resources/robot/unitree/go2>` | `UnitreeGo2Cfg` | 12 |
| {doc}`UnitreeG1 </resources/robot/unitree/g1>` | `UnitreeG1Cfg` | 29 |
| {doc}`UnitreeH1_2 </resources/robot/unitree/h1_2>` | `UnitreeH12Cfg` | 27 |
| {doc}`ANYmalC </resources/robot/anybotics/anymal_c>` | `ANYmalCCfg` | 12 |
| {doc}`MicroDuck </resources/robot/pollen_robotics/microduck>` | `MicroDuckCfg` | 14 |

```bash
embodichain preview-robot --robot UnitreeGo2
```

Each configuration owns the simulation asset, floating base, default standing
pose, `locomotion` control group and effective PD drive defaults previously
defined by its flat-velocity task. The same model configuration serves Default
and Newton deployments. G1 and H1_2 identify the specific bundled models above;
they do not select other hardware revisions implicitly.

Task YAML selects `robot.class_type` and may override `joint_drive_props`.
Tasks continue to own action scaling, observation layout, commands, rewards,
randomization and illegal-contact rules. Contact sensors remain environment
declarations. The model profiles are simulation presets, not certified hardware
calibrations or implementations of Isaac Lab's learned or BAM actuators.

The backend-free profile reader is shared with pure Torch task contracts.
Source-ordered `asset.init_qpos` and the ordered
`robot.joint_names` / `robot.default_joint_position` pair have distinct roles;
keep their ordering when changing a model. Foot and IMU reference points are
stored alongside the model. The presets retain `build_pk_chain=False` and
provide no limb IK solver.

See the {doc}`API reference </api_reference/locomotion_robots>` for the public
configuration classes and model-profile reader.

## Render Settings

The individual previews use DexSim OfflineRT (`rt`) with shared lighting and
render settings: 1 sample per pixel, DLSS denoising, tone mapping disabled and
exposure 1. Each model uses its default joint pose and is framed independently,
so the images do not show a common physical scale. RGB captures have no
additional gamma or color correction.